R : Selecting Informative Predictors Using Recursive Feature Elimination
John Pauline Pineda
December 19, 2022
- 1.
Table of Contents
- 1.1 Sample Data
- 1.2 Data Quality Assessment
- 1.3 Data Preprocessing
- 1.4 Data Exploration
- 1.5 Recursive Feature
Elimination (RFE)
- 1.5.1 Random Forest Without RFE (RF_FULL)
- 1.5.2 Linear Discriminant Analysis Without RFE (LDA_FULL)
- 1.5.3 Naive Bayes Without RFE (NB_FULL)
- 1.5.4 Logistic Regression Without RFE (LR_FULL)
- 1.5.5 Support Vector Machine - Radial Basis Function Kernel Without RFE (SVM_R_FULL)
- 1.5.6 K-Nearest Neighbors Without RFE (KNN_FULL)
- 1.5.7 Random Forest With RFE (RF_RFE)
- 1.5.8 Linear Discriminant Analysis With RFE (LDA_RFE)
- 1.5.9 Naive Bayes With RFE (NB_RFE)
- 1.5.10 Logistic Regression With RFE (LR_RFE)
- 1.5.11 Support Vector Machine - Radial Basis Function Kernel With RFE (SVM_R_RFE)
- 1.5.12 K-Nearest Neighbors With RFE (KNN_RFE)
- 1.6 Model Evaluation Summary
- 2. References
1. Table of Contents
This project implements Recursive Feature Elimination in selecting informative predictors for a modelling problem using the Random Forest, Linear Discriminant Analysis, Naive Bayes, Logistic Regression, Support Vector Machine and K-Nearest Neighbors model structures in R. The resulting predictions derived from the candidate models applying Recursive Feature Elimination were evaluated in terms of their discrimination power using the receiver operating characteristic (ROC) curve area under the curve (AUC) metric. The ROC AUC values were compared to those of the baseline models which made use of the full data without any form of feature selection, or implemented a model-specific feature selection process.
Feature selection is the process of reducing the number of input variables by eliminating redundant or unimportant features and narrowing down the set of features to those most relevant to the machine learning approach, resulting to simpler explainable models, shorter training times due to a more precise subset of features, reduction of variance, increase in precision estimates and more robust solution against the curse of high dimensionality. The algorithms applied in this study (mostly contained in the caret package) are wrapper feature selection methods which attempt to consider the task of selecting feature subsets as a search problem, whereby their quality is assessed with the preparation, evaluation, and comparison between different feature combinations.
1.1 Sample Data
The AlzheimerDisease dataset from the AppliedPredictiveModeling package was used for this illustrated example.
Preliminary dataset assessment:
[A] 333 rows (observations)
[A.1] Train Set = 267 observations
[A.2] Test Set = 66 observations
[B] 128 columns (variables)
[B.1] 1/128 response = Class variable (factor)
[B.1.1] Levels = Class=Control < Class=Impaired
[B.2] 127/128 predictors = All remaining variables (3/127 factor + 124/127 numeric)
##################################
# Loading R libraries
##################################
library(AppliedPredictiveModeling)
library(caret)
library(rpart)
library(lattice)
library(dplyr)
library(tidyr)
library(moments)
library(skimr)
library(RANN)
library(pls)
library(corrplot)
library(tidyverse)
library(lares)
library(DMwR)
library(gridExtra)
library(rattle)
library(rpart.plot)
library(RColorBrewer)
library(stats)
library(nnet)
library(elasticnet)
library(earth)
library(party)
library(kernlab)
library(randomForest)
library(Cubist)
library(pROC)
library(mda)
library(klaR)
library(pamr)
##################################
# Loading source and
# formulating the train set
##################################
data(AlzheimerDisease)
Alzheimer <- predictors
Alzheimer$Class <- diagnosis
##################################
# Decomposing the Genotype factor
# into binary dummy variables
##################################
## Decompose the genotype factor into binary dummy variables
Alzheimer$E2 <- Alzheimer$E3 <- Alzheimer$E4 <- 0
Alzheimer$E2[grepl("2", Alzheimer$Genotype)] <- 1
Alzheimer$E3[grepl("3", Alzheimer$Genotype)] <- 1
Alzheimer$E4[grepl("4", Alzheimer$Genotype)] <- 1
Alzheimer_Original <- Alzheimer
##################################
# Removing baseline predictors
##################################
Alzheimer <- Alzheimer[,!(names(Alzheimer) %in% c("Genotype", "age", "tau", "p_tau", "Ab_42", "male"))]
##################################
# Partitoning the data into
# train and test sets
##################################
set.seed(12345678)
Alzheimer_Train_Index <- createDataPartition(Alzheimer$Class,p=0.8)[[1]]
Alzheimer_Train <- Alzheimer[ Alzheimer_Train_Index, ]
Alzheimer_Test <- Alzheimer[-Alzheimer_Train_Index, ]
##################################
# Performing a general exploration of the train set
##################################
dim(Alzheimer_Train)## [1] 267 128str(Alzheimer_Train)## 'data.frame': 267 obs. of 128 variables:
## $ ACE_CD143_Angiotensin_Converti : num 2.003 1.562 1.521 2.401 0.431 ...
## $ ACTH_Adrenocorticotropic_Hormon : num -1.386 -1.386 -1.715 -0.968 -1.273 ...
## $ AXL : num 1.098 0.683 -0.145 0.191 -0.222 ...
## $ Adiponectin : num -5.36 -5.02 -5.81 -4.78 -5.22 ...
## $ Alpha_1_Antichymotrypsin : num 1.74 1.46 1.19 2.13 1.31 ...
## $ Alpha_1_Antitrypsin : num -12.6 -11.9 -13.6 -11.1 -12.1 ...
## $ Alpha_1_Microglobulin : num -2.58 -3.24 -2.88 -2.34 -2.55 ...
## $ Alpha_2_Macroglobulin : num -72.7 -154.6 -136.5 -144.9 -154.6 ...
## $ Angiopoietin_2_ANG_2 : num 1.0647 0.7419 0.8329 0.9555 -0.0513 ...
## $ Angiotensinogen : num 2.51 2.46 1.98 2.86 2.52 ...
## $ Apolipoprotein_A_IV : num -1.43 -1.66 -1.66 -1.17 -1.39 ...
## $ Apolipoprotein_A1 : num -7.4 -7.05 -7.68 -6.73 -7.4 ...
## $ Apolipoprotein_A2 : num -0.2614 -0.8675 -0.6539 0.0953 -0.2744 ...
## $ Apolipoprotein_B : num -4.62 -6.75 -3.98 -3.38 -2.96 ...
## $ Apolipoprotein_CI : num -1.273 -1.273 -1.715 -0.755 -1.661 ...
## $ Apolipoprotein_CIII : num -2.31 -2.34 -2.75 -1.51 -2.31 ...
## $ Apolipoprotein_D : num 2.08 1.34 1.34 1.63 1.92 ...
## $ Apolipoprotein_E : num 3.755 3.097 2.753 3.067 0.591 ...
## $ Apolipoprotein_H : num -0.1573 -0.5754 -0.3448 0.6626 0.0972 ...
## $ B_Lymphocyte_Chemoattractant_BL : num 2.3 1.67 1.67 2.3 2.48 ...
## $ BMP_6 : num -2.2 -1.73 -2.06 -1.24 -1.88 ...
## $ Beta_2_Microglobulin : num 0.693 0.47 0.336 0.336 -0.545 ...
## $ Betacellulin : int 34 53 49 67 51 41 42 58 32 43 ...
## $ C_Reactive_Protein : num -4.07 -6.65 -8.05 -4.34 -7.56 ...
## $ CD40 : num -0.796 -1.273 -1.242 -0.924 -1.784 ...
## $ CD5L : num 0.0953 -0.6733 0.0953 0.3633 0.4055 ...
## $ Calbindin : num 33.2 25.3 22.2 21.8 13.2 ...
## $ Calcitonin : num 1.39 3.61 2.12 1.31 1.63 ...
## $ CgA : num 398 466 348 443 138 ...
## $ Clusterin_Apo_J : num 3.56 3.04 2.77 3.04 2.56 ...
## $ Complement_3 : num -10.4 -16.1 -16.1 -12.8 -12 ...
## $ Complement_Factor_H : num 3.57 3.6 4.47 7.25 3.57 ...
## $ Connective_Tissue_Growth_Factor : num 0.531 0.588 0.642 0.916 0.993 ...
## $ Cortisol : num 10 12 10 11 13 4.9 13 12 6.8 12 ...
## $ Creatine_Kinase_MB : num -1.71 -1.75 -1.38 -1.63 -1.67 ...
## $ Cystatin_C : num 9.04 9.07 8.95 8.98 7.84 ...
## $ EGF_R : num -0.135 -0.37 -0.733 -0.621 -1.111 ...
## $ EN_RAGE : num -3.69 -3.82 -4.76 -2.36 -3.44 ...
## $ ENA_78 : num -1.35 -1.36 -1.39 -1.34 -1.36 ...
## $ Eotaxin_3 : int 53 62 62 64 57 64 64 64 82 73 ...
## $ FAS : num -0.0834 -0.5276 -0.6349 -0.1278 -0.3285 ...
## $ FSH_Follicle_Stimulation_Hormon : num -0.652 -1.627 -1.563 -0.976 -1.683 ...
## $ Fas_Ligand : num 3.1 2.98 1.36 4.04 2.41 ...
## $ Fatty_Acid_Binding_Protein : num 2.521 2.248 0.906 2.635 0.624 ...
## $ Ferritin : num 3.33 3.93 3.18 2.69 1.85 ...
## $ Fetuin_A : num 1.28 1.19 1.41 2.15 1.48 ...
## $ Fibrinogen : num -7.04 -8.05 -7.2 -6.98 -6.44 ...
## $ GRO_alpha : num 1.38 1.37 1.41 1.4 1.4 ...
## $ Gamma_Interferon_induced_Monokin: num 2.95 2.72 2.76 2.85 2.82 ...
## $ Glutathione_S_Transferase_alpha : num 1.064 0.867 0.889 1.236 1.154 ...
## $ HB_EGF : num 6.56 8.75 7.75 7.25 6.41 ...
## $ HCC_4 : num -3.04 -4.07 -3.65 -3.15 -3.08 ...
## $ Hepatocyte_Growth_Factor_HGF : num 0.5878 0.5306 0.0953 0.5306 0.0953 ...
## $ I_309 : num 3.43 3.14 2.4 3.76 2.71 ...
## $ ICAM_1 : num -0.1908 -0.462 -0.462 0.0972 -0.9351 ...
## $ IGF_BP_2 : num 5.61 5.35 5.18 5.42 5.06 ...
## $ IL_11 : num 5.12 4.94 4.67 7.07 6.1 ...
## $ IL_13 : num 1.28 1.27 1.27 1.31 1.28 ...
## $ IL_16 : num 4.19 2.88 2.62 4.74 2.67 ...
## $ IL_17E : num 5.73 6.71 4.15 4.2 3.64 ...
## $ IL_1alpha : num -6.57 -8.05 -8.18 -6.94 -8.18 ...
## $ IL_3 : num -3.24 -3.91 -4.65 -3 -3.86 ...
## $ IL_4 : num 2.48 2.4 1.82 2.71 1.21 ...
## $ IL_5 : num 1.099 0.693 -0.248 1.163 -0.4 ...
## $ IL_6 : num 0.2694 0.0962 0.1857 -0.072 0.1857 ...
## $ IL_6_Receptor : num 0.6428 0.4312 0.0967 0.0967 -0.5173 ...
## $ IL_7 : num 4.81 3.71 1.01 4.29 2.78 ...
## $ IL_8 : num 1.71 1.68 1.69 1.76 1.71 ...
## $ IP_10_Inducible_Protein_10 : num 6.24 5.69 5.05 6.37 5.48 ...
## $ IgA : num -6.81 -6.38 -6.32 -4.65 -5.81 ...
## $ Insulin : num -0.626 -0.943 -1.447 -0.3 -1.341 ...
## $ Kidney_Injury_Molecule_1_KIM_1 : num -1.2 -1.2 -1.19 -1.16 -1.12 ...
## $ LOX_1 : num 1.705 1.526 1.163 1.361 0.642 ...
## $ Leptin : num -1.529 -1.466 -1.662 -0.915 -1.361 ...
## $ Lipoprotein_a : num -4.27 -4.93 -5.84 -2.94 -4.51 ...
## $ MCP_1 : num 6.74 6.85 6.77 6.72 6.54 ...
## $ MCP_2 : num 1.981 1.809 0.401 2.221 2.334 ...
## $ MIF : num -1.24 -1.9 -2.3 -1.9 -2.04 ...
## $ MIP_1alpha : num 4.97 3.69 4.05 6.45 4.6 ...
## $ MIP_1beta : num 3.26 3.14 2.4 3.53 2.89 ...
## $ MMP_2 : num 4.48 3.78 2.87 3.69 2.92 ...
## $ MMP_3 : num -2.21 -2.47 -2.3 -1.56 -3.04 ...
## $ MMP10 : num -3.27 -3.65 -2.73 -2.62 -3.32 ...
## $ MMP7 : num -3.774 -5.968 -4.03 -0.222 -1.922 ...
## $ Myoglobin : num -1.897 -0.755 -1.386 -1.772 -1.139 ...
## $ NT_proBNP : num 4.55 4.22 4.25 4.47 4.19 ...
## $ NrCAM : num 5 5.21 4.74 5.2 3.26 ...
## $ Osteopontin : num 5.36 6 5.02 5.69 4.74 ...
## $ PAI_1 : num 1.0035 -0.0306 0.4384 0.2523 0.4384 ...
## $ PAPP_A : num -2.9 -2.81 -2.94 -2.94 -2.94 ...
## $ PLGF : num 4.44 4.03 4.51 4.8 4.39 ...
## $ PYY : num 3.22 3.14 2.89 3.66 3.33 ...
## $ Pancreatic_polypeptide : num 0.579 0.336 -0.892 0.262 -0.478 ...
## $ Prolactin : num 0 -0.511 -0.139 0.182 -0.151 ...
## $ Prostatic_Acid_Phosphatase : num -1.62 -1.74 -1.64 -1.7 -1.76 ...
## $ Protein_S : num -1.78 -2.46 -2.26 -1.66 -2.36 ...
## $ Pulmonary_and_Activation_Regulat: num -0.844 -2.303 -1.661 -0.562 -1.171 ...
## $ RANTES : num -6.21 -6.94 -6.65 -6.32 -6.5 ...
## $ Resistin : num -16.5 -16 -16.5 -11.1 -11.3 ...
## [list output truncated]summary(Alzheimer_Train)## ACE_CD143_Angiotensin_Converti ACTH_Adrenocorticotropic_Hormon
## Min. :-0.6756 Min. :-2.207
## 1st Qu.: 0.9462 1st Qu.:-1.715
## Median : 1.3013 Median :-1.561
## Mean : 1.3198 Mean :-1.538
## 3rd Qu.: 1.7191 3rd Qu.:-1.347
## Max. : 2.8398 Max. :-0.844
## AXL Adiponectin Alpha_1_Antichymotrypsin
## Min. :-0.9230 Min. :-6.725 Min. :0.2624
## 1st Qu.: 0.0000 1st Qu.:-5.669 1st Qu.:1.1314
## Median : 0.2804 Median :-5.185 Median :1.3610
## Mean : 0.3093 Mean :-5.201 Mean :1.3605
## 3rd Qu.: 0.6077 3rd Qu.:-4.780 3rd Qu.:1.5892
## Max. : 1.5214 Max. :-3.507 Max. :2.3026
## Alpha_1_Antitrypsin Alpha_1_Microglobulin Alpha_2_Macroglobulin
## Min. :-17.028 Min. :-4.343 Min. :-289.68
## 1st Qu.:-14.071 1st Qu.:-3.270 1st Qu.:-186.64
## Median :-13.004 Median :-2.937 Median :-160.01
## Mean :-13.052 Mean :-2.932 Mean :-158.61
## 3rd Qu.:-12.096 3rd Qu.:-2.590 3rd Qu.:-134.62
## Max. : -8.192 Max. :-1.772 Max. : -59.46
## Angiopoietin_2_ANG_2 Angiotensinogen Apolipoprotein_A_IV Apolipoprotein_A1
## Min. :-0.5447 Min. :1.752 Min. :-2.9565 Min. :-8.680
## 1st Qu.: 0.4700 1st Qu.:2.119 1st Qu.:-2.1203 1st Qu.:-7.763
## Median : 0.6419 Median :2.320 Median :-1.8326 Median :-7.470
## Mean : 0.6730 Mean :2.318 Mean :-1.8544 Mean :-7.483
## 3rd Qu.: 0.8755 3rd Qu.:2.497 3rd Qu.:-1.6094 3rd Qu.:-7.209
## Max. : 1.5261 Max. :2.881 Max. :-0.7765 Max. :-6.166
## Apolipoprotein_A2 Apolipoprotein_B Apolipoprotein_CI Apolipoprotein_CIII
## Min. :-1.8971 Min. :-9.937 Min. :-3.3242 Min. :-3.689
## 1st Qu.:-0.9676 1st Qu.:-6.630 1st Qu.:-1.8326 1st Qu.:-2.773
## Median :-0.6733 Median :-5.703 Median :-1.6094 Median :-2.526
## Mean :-0.6354 Mean :-5.578 Mean :-1.5833 Mean :-2.494
## 3rd Qu.:-0.3147 3rd Qu.:-4.539 3rd Qu.:-1.3667 3rd Qu.:-2.207
## Max. : 0.9555 Max. :-2.153 Max. :-0.2744 Max. :-1.238
## Apolipoprotein_D Apolipoprotein_E Apolipoprotein_H
## Min. :0.470 Min. :0.5911 Min. :-2.23379
## 1st Qu.:1.209 1st Qu.:2.3344 1st Qu.:-0.59782
## Median :1.411 Median :2.8181 Median :-0.37005
## Mean :1.440 Mean :2.8062 Mean :-0.32122
## 3rd Qu.:1.668 3rd Qu.:3.2863 3rd Qu.:-0.06112
## Max. :2.272 Max. :5.4442 Max. : 0.92696
## B_Lymphocyte_Chemoattractant_BL BMP_6 Beta_2_Microglobulin
## Min. :0.7318 Min. :-2.7612 Min. :-0.54473
## 1st Qu.:1.6731 1st Qu.:-2.1516 1st Qu.:-0.04082
## Median :1.9805 Median :-1.8774 Median : 0.18232
## Mean :2.0175 Mean :-1.9114 Mean : 0.16757
## 3rd Qu.:2.3714 3rd Qu.:-1.6753 3rd Qu.: 0.33647
## Max. :4.0237 Max. :-0.8166 Max. : 0.99325
## Betacellulin C_Reactive_Protein CD40 CD5L
## Min. :10.00 Min. :-8.517 Min. :-1.8644 Min. :-1.23787
## 1st Qu.:42.00 1st Qu.:-6.645 1st Qu.:-1.3761 1st Qu.:-0.35667
## Median :51.00 Median :-5.843 Median :-1.2734 Median :-0.06188
## Mean :51.01 Mean :-5.874 Mean :-1.2584 Mean :-0.05310
## 3rd Qu.:59.00 3rd Qu.:-5.083 3rd Qu.:-1.1238 3rd Qu.: 0.26236
## Max. :82.00 Max. :-2.937 Max. :-0.5475 Max. : 1.16315
## Calbindin Calcitonin CgA Clusterin_Apo_J
## Min. :10.96 Min. :-0.7134 Min. :135.6 Min. :1.872
## 1st Qu.:19.77 1st Qu.: 0.9555 1st Qu.:278.0 1st Qu.:2.708
## Median :22.25 Median : 1.6487 Median :331.5 Median :2.890
## Mean :22.43 Mean : 1.6788 Mean :333.3 Mean :2.882
## 3rd Qu.:24.80 3rd Qu.: 2.2824 3rd Qu.:392.1 3rd Qu.:3.045
## Max. :33.78 Max. : 3.8918 Max. :535.4 Max. :3.584
## Complement_3 Complement_Factor_H Connective_Tissue_Growth_Factor
## Min. :-23.387 Min. :-0.8387 Min. :0.1823
## 1st Qu.:-17.567 1st Qu.: 2.7531 1st Qu.:0.6419
## Median :-15.524 Median : 3.6000 Median :0.7885
## Mean :-15.610 Mean : 3.5541 Mean :0.7739
## 3rd Qu.:-13.882 3rd Qu.: 4.2548 3rd Qu.:0.9163
## Max. : -9.563 Max. : 7.6238 Max. :1.4110
## Cortisol Creatine_Kinase_MB Cystatin_C EGF_R
## Min. : 0.10 Min. :-1.872 Min. :7.432 Min. :-1.36135
## 1st Qu.: 9.80 1st Qu.:-1.724 1st Qu.:8.321 1st Qu.:-0.85727
## Median :12.00 Median :-1.671 Median :8.564 Median :-0.68354
## Mean :11.98 Mean :-1.674 Mean :8.586 Mean :-0.70130
## 3rd Qu.:14.00 3rd Qu.:-1.626 3rd Qu.:8.839 3rd Qu.:-0.54612
## Max. :29.00 Max. :-1.384 Max. :9.694 Max. :-0.06112
## EN_RAGE ENA_78 Eotaxin_3 FAS
## Min. :-8.3774 Min. :-1.405 Min. : 7.00 Min. :-1.5141
## 1st Qu.:-4.1997 1st Qu.:-1.381 1st Qu.: 44.00 1st Qu.:-0.7133
## Median :-3.6497 Median :-1.374 Median : 59.00 Median :-0.5276
## Mean :-3.6353 Mean :-1.372 Mean : 58.17 Mean :-0.5291
## 3rd Qu.:-3.1466 3rd Qu.:-1.364 3rd Qu.: 70.00 3rd Qu.:-0.3147
## Max. :-0.3857 Max. :-1.339 Max. :107.00 Max. : 0.3365
## FSH_Follicle_Stimulation_Hormon Fas_Ligand Fatty_Acid_Binding_Protein
## Min. :-2.11511 Min. :-0.1536 Min. :-1.0441
## 1st Qu.:-1.46606 1st Qu.: 2.3415 1st Qu.: 0.7998
## Median :-1.13570 Median : 3.1015 Median : 1.3865
## Mean :-1.14259 Mean : 2.9680 Mean : 1.3529
## 3rd Qu.:-0.87620 3rd Qu.: 3.6950 3rd Qu.: 1.8847
## Max. : 0.09715 Max. : 7.6328 Max. : 3.7055
## Ferritin Fetuin_A Fibrinogen GRO_alpha
## Min. :0.6077 Min. :0.470 Min. :-8.874 Min. :1.271
## 1st Qu.:2.2895 1st Qu.:1.099 1st Qu.:-7.717 1st Qu.:1.351
## Median :2.7749 Median :1.308 Median :-7.323 Median :1.382
## Mean :2.7646 Mean :1.350 Mean :-7.356 Mean :1.378
## 3rd Qu.:3.2915 3rd Qu.:1.609 3rd Qu.:-7.013 3rd Qu.:1.406
## Max. :4.6333 Max. :2.251 Max. :-5.843 Max. :1.495
## Gamma_Interferon_induced_Monokin Glutathione_S_Transferase_alpha
## Min. :2.393 Min. :0.5238
## 1st Qu.:2.707 1st Qu.:0.8439
## Median :2.783 Median :0.9677
## Mean :2.786 Mean :0.9512
## 3rd Qu.:2.873 3rd Qu.:1.0344
## Max. :3.065 Max. :1.3176
## HB_EGF HCC_4 Hepatocyte_Growth_Factor_HGF I_309
## Min. : 2.103 Min. :-4.510 Min. :-0.6349 Min. :1.758
## 1st Qu.: 5.786 1st Qu.:-3.730 1st Qu.: 0.0000 1st Qu.:2.708
## Median : 6.703 Median :-3.507 Median : 0.1823 Median :2.944
## Mean : 6.833 Mean :-3.500 Mean : 0.1963 Mean :2.958
## 3rd Qu.: 7.865 3rd Qu.:-3.270 3rd Qu.: 0.4055 3rd Qu.:3.219
## Max. :10.695 Max. :-2.120 Max. : 0.8755 Max. :4.143
## ICAM_1 IGF_BP_2 IL_11 IL_13
## Min. :-1.5332 Min. :4.635 Min. :1.755 Min. :1.259
## 1st Qu.:-0.8298 1st Qu.:5.179 1st Qu.:3.706 1st Qu.:1.274
## Median :-0.5903 Median :5.323 Median :4.805 Median :1.283
## Mean :-0.5908 Mean :5.317 Mean :4.725 Mean :1.284
## 3rd Qu.:-0.3828 3rd Qu.:5.453 3rd Qu.:5.776 3rd Qu.:1.290
## Max. : 0.5171 Max. :5.948 Max. :8.491 Max. :1.321
## IL_16 IL_17E IL_1alpha IL_3
## Min. :1.187 Min. :1.052 Min. :-8.517 Min. :-5.915
## 1st Qu.:2.521 1st Qu.:4.149 1st Qu.:-7.824 1st Qu.:-4.269
## Median :2.909 Median :4.749 Median :-7.524 Median :-3.912
## Mean :2.929 Mean :4.855 Mean :-7.514 Mean :-3.941
## 3rd Qu.:3.351 3rd Qu.:5.631 3rd Qu.:-7.264 3rd Qu.:-3.631
## Max. :4.937 Max. :8.952 Max. :-5.952 Max. :-2.453
## IL_4 IL_5 IL_6 IL_6_Receptor
## Min. :0.5306 Min. :-1.4271 Min. :-1.5343 Min. :-0.67562
## 1st Qu.:1.4586 1st Qu.:-0.1221 1st Qu.:-0.4127 1st Qu.:-0.12541
## Median :1.8083 Median : 0.1823 Median :-0.1599 Median : 0.09669
## Mean :1.7732 Mean : 0.1866 Mean :-0.1540 Mean : 0.09492
## 3rd Qu.:2.1459 3rd Qu.: 0.4700 3rd Qu.: 0.1410 3rd Qu.: 0.35404
## Max. :3.0445 Max. : 1.9459 Max. : 1.8138 Max. : 0.83099
## IL_7 IL_8 IP_10_Inducible_Protein_10 IgA
## Min. :0.5598 Min. :1.574 Min. :4.317 Min. :-10.520
## 1st Qu.:2.1548 1st Qu.:1.680 1st Qu.:5.398 1st Qu.: -6.645
## Median :2.7934 Median :1.705 Median :5.753 Median : -6.119
## Mean :2.8392 Mean :1.704 Mean :5.755 Mean : -6.121
## 3rd Qu.:3.7055 3rd Qu.:1.728 3rd Qu.:6.064 3rd Qu.: -5.573
## Max. :5.7056 Max. :1.807 Max. :7.501 Max. : -4.200
## Insulin Kidney_Injury_Molecule_1_KIM_1 LOX_1
## Min. :-2.1692 Min. :-1.256 Min. :0.000
## 1st Qu.:-1.4466 1st Qu.:-1.204 1st Qu.:1.030
## Median :-1.2462 Median :-1.183 Median :1.281
## Mean :-1.2329 Mean :-1.185 Mean :1.283
## 3rd Qu.:-1.0340 3rd Qu.:-1.164 3rd Qu.:1.526
## Max. :-0.1586 Max. :-1.105 Max. :2.272
## Leptin Lipoprotein_a MCP_1 MCP_2
## Min. :-2.1468 Min. :-6.812 Min. :5.826 Min. :0.4006
## 1st Qu.:-1.6996 1st Qu.:-5.308 1st Qu.:6.319 1st Qu.:1.5304
## Median :-1.5047 Median :-4.605 Median :6.494 Median :1.8528
## Mean :-1.5042 Mean :-4.417 Mean :6.497 Mean :1.8691
## 3rd Qu.:-1.3295 3rd Qu.:-3.490 3rd Qu.:6.678 3rd Qu.:2.1821
## Max. :-0.6206 Max. :-1.386 Max. :7.230 Max. :4.0237
## MIF MIP_1alpha MIP_1beta MMP_2
## Min. :-2.847 Min. :0.9346 Min. :1.946 Min. :0.09809
## 1st Qu.:-2.120 1st Qu.:3.3377 1st Qu.:2.565 1st Qu.:2.33214
## Median :-1.897 Median :4.0495 Median :2.833 Median :2.81512
## Mean :-1.864 Mean :4.0489 Mean :2.814 Mean :2.87534
## 3rd Qu.:-1.661 3rd Qu.:4.6857 3rd Qu.:3.045 3rd Qu.:3.55121
## Max. :-0.844 Max. :6.7959 Max. :4.007 Max. :5.35895
## MMP_3 MMP10 MMP7 Myoglobin
## Min. :-4.4228 Min. :-4.934 Min. :-8.3975 Min. :-3.1701
## 1st Qu.:-2.7489 1st Qu.:-3.938 1st Qu.:-4.8199 1st Qu.:-2.0402
## Median :-2.4534 Median :-3.650 Median :-3.7735 Median :-1.4697
## Mean :-2.4455 Mean :-3.635 Mean :-3.7894 Mean :-1.3671
## 3rd Qu.:-2.1203 3rd Qu.:-3.352 3rd Qu.:-2.7140 3rd Qu.:-0.7988
## Max. :-0.5276 Max. :-2.207 Max. :-0.2222 Max. : 1.7750
## NT_proBNP NrCAM Osteopontin PAI_1
## Min. :3.178 Min. :2.639 Min. :4.111 Min. :-0.99085
## 1st Qu.:4.350 1st Qu.:3.998 1st Qu.:4.963 1st Qu.:-0.16655
## Median :4.554 Median :4.394 Median :5.187 Median : 0.09396
## Mean :4.552 Mean :4.362 Mean :5.204 Mean : 0.07743
## 3rd Qu.:4.775 3rd Qu.:4.749 3rd Qu.:5.442 3rd Qu.: 0.32005
## Max. :5.886 Max. :6.011 Max. :6.308 Max. : 1.16611
## PAPP_A PLGF PYY Pancreatic_polypeptide
## Min. :-3.311 Min. :2.485 Min. :2.186 Min. :-2.12026
## 1st Qu.:-2.936 1st Qu.:3.638 1st Qu.:2.833 1st Qu.:-0.52763
## Median :-2.871 Median :3.871 Median :2.996 Median :-0.04082
## Mean :-2.854 Mean :3.912 Mean :3.015 Mean :-0.01323
## 3rd Qu.:-2.749 3rd Qu.:4.205 3rd Qu.:3.178 3rd Qu.: 0.53063
## Max. :-2.520 Max. :5.170 Max. :3.932 Max. : 1.93152
## Prolactin Prostatic_Acid_Phosphatase Protein_S
## Min. :-1.30933 Min. :-1.934 Min. :-3.338
## 1st Qu.:-0.13926 1st Qu.:-1.717 1st Qu.:-2.464
## Median : 0.00000 Median :-1.690 Median :-2.259
## Mean : 0.04495 Mean :-1.685 Mean :-2.240
## 3rd Qu.: 0.25799 3rd Qu.:-1.654 3rd Qu.:-2.000
## Max. : 0.99325 Max. :-1.424 Max. :-1.221
## Pulmonary_and_Activation_Regulat RANTES Resistin
## Min. :-2.5133 Min. :-7.222 Min. :-34.967
## 1st Qu.:-1.8326 1st Qu.:-6.725 1st Qu.:-21.468
## Median :-1.5141 Median :-6.502 Median :-17.466
## Mean :-1.4880 Mean :-6.511 Mean :-17.641
## 3rd Qu.:-1.1712 3rd Qu.:-6.320 3rd Qu.:-13.501
## Max. :-0.2744 Max. :-5.547 Max. : -2.239
## S100b SGOT SHBG SOD
## Min. :0.1874 Min. :-1.3471 Min. :-4.135 Min. :4.317
## 1st Qu.:1.0012 1st Qu.:-0.6349 1st Qu.:-2.813 1st Qu.:5.094
## Median :1.2544 Median :-0.4005 Median :-2.489 Median :5.366
## Mean :1.2505 Mean :-0.4057 Mean :-2.477 Mean :5.336
## 3rd Qu.:1.4996 3rd Qu.:-0.1985 3rd Qu.:-2.120 3rd Qu.:5.583
## Max. :2.3726 Max. : 0.7419 Max. :-1.109 Max. :6.317
## Serum_Amyloid_P Sortilin Stem_Cell_Factor TGF_alpha
## Min. :-7.506 Min. :1.654 Min. :2.251 Min. : 6.843
## 1st Qu.:-6.377 1st Qu.:3.343 1st Qu.:3.045 1st Qu.: 8.859
## Median :-6.032 Median :3.867 Median :3.296 Median : 9.919
## Mean :-6.017 Mean :3.852 Mean :3.301 Mean : 9.801
## 3rd Qu.:-5.655 3rd Qu.:4.371 3rd Qu.:3.526 3rd Qu.:10.695
## Max. :-4.646 Max. :6.225 Max. :4.277 Max. :13.827
## TIMP_1 TNF_RII TRAIL_R3 TTR_prealbumin
## Min. : 1.742 Min. :-1.6607 Min. :-1.2107 Min. :2.485
## 1st Qu.:10.490 1st Qu.:-0.8210 1st Qu.:-0.7008 1st Qu.:2.773
## Median :11.565 Median :-0.5978 Median :-0.5317 Median :2.833
## Mean :11.750 Mean :-0.5939 Mean :-0.5394 Mean :2.854
## 3rd Qu.:12.697 3rd Qu.:-0.3784 3rd Qu.:-0.3849 3rd Qu.:2.944
## Max. :18.881 Max. : 0.4700 Max. : 0.2694 Max. :3.332
## Tamm_Horsfall_Protein_THP Thrombomodulin Thrombopoietin
## Min. :-3.206 Min. :-2.0377 Min. :-1.53957
## 1st Qu.:-3.137 1st Qu.:-1.6256 1st Qu.:-0.88645
## Median :-3.117 Median :-1.4920 Median :-0.75100
## Mean :-3.116 Mean :-1.5050 Mean :-0.75419
## 3rd Qu.:-3.096 3rd Qu.:-1.3406 3rd Qu.:-0.62887
## Max. :-2.995 Max. :-0.8166 Max. : 0.09762
## Thymus_Expressed_Chemokine_TECK Thyroid_Stimulating_Hormone
## Min. :1.508 Min. :-6.190
## 1st Qu.:3.343 1st Qu.:-4.962
## Median :3.810 Median :-4.510
## Mean :3.848 Mean :-4.499
## 3rd Qu.:4.316 3rd Qu.:-4.017
## Max. :6.225 Max. :-1.715
## Thyroxine_Binding_Globulin Tissue_Factor Transferrin
## Min. :-2.4769 Min. :-0.2107 Min. :1.932
## 1st Qu.:-1.7720 1st Qu.: 0.8329 1st Qu.:2.708
## Median :-1.5141 Median : 1.2238 Median :2.890
## Mean :-1.4788 Mean : 1.1702 Mean :2.909
## 3rd Qu.:-1.2379 3rd Qu.: 1.4816 3rd Qu.:3.091
## Max. :-0.2107 Max. : 2.4849 Max. :3.761
## Trefoil_Factor_3_TFF3 VCAM_1 VEGF Vitronectin
## Min. :-4.744 Min. :1.723 Min. :11.83 Min. :-1.42712
## 1st Qu.:-4.135 1st Qu.:2.485 1st Qu.:15.77 1st Qu.:-0.51083
## Median :-3.863 Median :2.708 Median :17.08 Median :-0.30111
## Mean :-3.876 Mean :2.688 Mean :16.99 Mean :-0.28473
## 3rd Qu.:-3.650 3rd Qu.:2.890 3rd Qu.:18.10 3rd Qu.:-0.03564
## Max. :-2.957 Max. :3.689 Max. :22.38 Max. : 0.53063
## von_Willebrand_Factor Class E4 E3
## Min. :-4.991 Impaired: 73 Min. :0.0000 Min. :0.0000
## 1st Qu.:-4.200 Control :194 1st Qu.:0.0000 1st Qu.:1.0000
## Median :-3.912 Median :0.0000 Median :1.0000
## Mean :-3.906 Mean :0.4007 Mean :0.9176
## 3rd Qu.:-3.612 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :-2.957 Max. :1.0000 Max. :1.0000
## E2
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.161
## 3rd Qu.:0.000
## Max. :1.000##################################
# Performing a general exploration of the test set
##################################
dim(Alzheimer_Test)## [1] 66 128str(Alzheimer_Test)## 'data.frame': 66 obs. of 128 variables:
## $ ACE_CD143_Angiotensin_Converti : num 1.681 1.602 1.301 1.562 0.831 ...
## $ ACTH_Adrenocorticotropic_Hormon : num -1.61 -1.51 -1.77 -1.56 -1.97 ...
## $ AXL : num 0.6833 0.4495 -0.0201 0.5298 -0.2 ...
## $ Adiponectin : num -5.12 -5.57 -6.17 -6.07 -5.43 ...
## $ Alpha_1_Antichymotrypsin : num 1.281 1.163 1.253 0.875 1.253 ...
## $ Alpha_1_Antitrypsin : num -15.5 -12.1 -14.5 -14 -13.3 ...
## $ Alpha_1_Microglobulin : num -3.17 -2.36 -3.32 -3.19 -3.24 ...
## $ Alpha_2_Macroglobulin : num -98.4 -144.9 -204.1 -125.8 -186.6 ...
## $ Angiopoietin_2_ANG_2 : num 0.916 0.531 0.262 0.742 0 ...
## $ Angiotensinogen : num 2.38 2.26 2.03 2.25 2.05 ...
## $ Apolipoprotein_A_IV : num -2.12 -1.9 -2.04 -2.3 -1.9 ...
## $ Apolipoprotein_A1 : num -8.05 -7.14 -8.08 -7.9 -8.18 ...
## $ Apolipoprotein_A2 : num -1.238 -0.562 -0.844 -1.022 -1.022 ...
## $ Apolipoprotein_B : num -6.52 -6.41 -5.56 -5.36 -5.94 ...
## $ Apolipoprotein_CI : num -1.97 -1.51 -2.21 -1.9 -2.04 ...
## $ Apolipoprotein_CIII : num -3 -1.97 -2.94 -2.83 -2.8 ...
## $ Apolipoprotein_D : num 1.44 1.48 1.39 1.16 1.25 ...
## $ Apolipoprotein_E : num 2.37 3.7 1.33 3.19 1.53 ...
## $ Apolipoprotein_H : num -0.532 -0.125 -0.32 -0.517 -0.462 ...
## $ B_Lymphocyte_Chemoattractant_BL : num 1.981 2.182 1.853 1.274 0.927 ...
## $ BMP_6 : num -1.98 -1.88 -1.68 -2.65 -1.68 ...
## $ Beta_2_Microglobulin : num 0.642 0.182 -0.274 0.405 -0.223 ...
## $ Betacellulin : int 52 59 51 46 51 42 61 46 32 46 ...
## $ C_Reactive_Protein : num -6.21 -6.21 -6.12 -5.71 -6.93 ...
## $ CD40 : num -1.12 -1.24 -1.45 -1.07 -1.41 ...
## $ CD5L : num -0.3285 0 -1.2379 -0.0408 -0.1054 ...
## $ Calbindin : num 23.5 20.6 13.4 27.3 22.5 ...
## $ Calcitonin : num -0.151 4.111 2.303 3.091 1.281 ...
## $ CgA : num 334 323 289 412 342 ...
## $ Clusterin_Apo_J : num 2.83 3.04 2.56 3.14 2.48 ...
## $ Complement_3 : num -13.2 -13 -17.3 -14.8 -19.2 ...
## $ Complement_Factor_H : num 3.1 4.68 3.78 2.52 3.3 ...
## $ Connective_Tissue_Growth_Factor : num 0.531 0.693 0.956 0.588 0.875 ...
## $ Cortisol : num 14 17 9.2 11 12 16 6.1 10 9.8 13 ...
## $ Creatine_Kinase_MB : num -1.65 -1.63 -1.61 -1.59 -1.72 ...
## $ Cystatin_C : num 9.58 8.33 7.91 8.99 8.47 ...
## $ EGF_R : num -0.422 -0.785 -0.976 -0.561 -0.767 ...
## $ EN_RAGE : num -2.94 -3.77 -3.24 -4.2 -4.27 ...
## $ ENA_78 : num -1.37 -1.38 -1.36 -1.37 -1.39 ...
## $ Eotaxin_3 : int 44 70 70 36 39 76 44 62 82 36 ...
## $ FAS : num -0.478 -0.0726 -0.5798 -0.5276 -0.9416 ...
## $ FSH_Follicle_Stimulation_Hormon : num -0.59 -0.652 -1.559 -1.505 -1.361 ...
## $ Fas_Ligand : num 2.537 2.792 2.073 0.288 3.867 ...
## $ Fatty_Acid_Binding_Protein : num 0.624 1.143 0.624 2.379 1.424 ...
## $ Ferritin : num 3.14 2.52 1.9 4.29 1.69 ...
## $ Fetuin_A : num 0.742 1.386 1.194 0.993 1.131 ...
## $ Fibrinogen : num -7.8 -7.06 -7.45 -7.8 -7.35 ...
## $ GRO_alpha : num 1.37 1.35 1.37 1.37 1.38 ...
## $ Gamma_Interferon_induced_Monokin: num 2.89 2.78 2.69 2.69 2.7 ...
## $ Glutathione_S_Transferase_alpha : num 0.708 1.034 1.13 0.676 0.844 ...
## $ HB_EGF : num 5.95 6.11 6.11 6.56 7.75 ...
## $ HCC_4 : num -3.82 -3.24 -3.77 -3.54 -3.73 ...
## $ Hepatocyte_Growth_Factor_HGF : num 0.4055 0.1823 -0.0834 0.47 -0.1054 ...
## $ I_309 : num 3.37 3 2.83 3.14 2.83 ...
## $ ICAM_1 : num -0.857 -1.111 -0.561 -0.357 -1.064 ...
## $ IGF_BP_2 : num 5.42 5.38 5.41 5.14 4.93 ...
## $ IL_11 : num 6.22 4.59 4.1 4.52 3.22 ...
## $ IL_13 : num 1.31 1.27 1.27 1.29 1.27 ...
## $ IL_16 : num 2.44 3.48 2.88 2.81 1.9 ...
## $ IL_17E : num 4.7 3.64 5.73 4.8 4.15 ...
## $ IL_1alpha : num -7.6 -7.37 -7.85 -7.68 -8.33 ...
## $ IL_3 : num -4.27 -4.02 -4.51 -3.58 -4.69 ...
## $ IL_4 : num 1.48 1.81 1.81 2.04 1.21 ...
## $ IL_5 : num 0.788 0.182 0 0.693 -0.371 ...
## $ IL_6 : num -0.371 -1.534 0.422 -0.563 0.8 ...
## $ IL_6_Receptor : num 0.5752 0.0967 -0.5322 -0.1586 0.0967 ...
## $ IL_7 : num 2.34 2.15 1.56 3.71 2.15 ...
## $ IL_8 : num 1.72 1.7 1.69 1.69 1.7 ...
## $ IP_10_Inducible_Protein_10 : num 5.6 5.33 5.06 4.75 5.29 ...
## $ IgA : num -7.62 -5.36 -7.04 -5.6 -6.57 ...
## $ Insulin : num -1.485 -1.034 -1.569 -0.901 -1.865 ...
## $ Kidney_Injury_Molecule_1_KIM_1 : num -1.23 -1.16 -1.12 -1.21 -1.16 ...
## $ LOX_1 : num 1.224 1.131 0.588 1.308 1.308 ...
## $ Leptin : num -1.27 -1.54 -1.54 -1.36 -1.55 ...
## $ Lipoprotein_a : num -4.99 -4.2 -3.54 -4.87 -5.08 ...
## $ MCP_1 : num 6.78 6.38 6.61 6.41 6.78 ...
## $ MCP_2 : num 1.981 1.626 1.626 0.401 1.853 ...
## $ MIF : num -1.66 -1.61 -2.04 -1.97 -1.83 ...
## $ MIP_1alpha : num 4.93 4.35 3.41 2.28 1.68 ...
## $ MIP_1beta : num 3.22 2.77 2.83 2.48 2.77 ...
## $ MMP_2 : num 2.97 2.92 3.27 4.05 1.36 ...
## $ MMP_3 : num -1.77 -1.97 -2.04 -2.04 -2.21 ...
## $ MMP10 : num -4.07 -3.27 -3.02 -3.04 -4.07 ...
## $ MMP7 : num -6.86 -3 -4.36 -3.35 -4.45 ...
## $ Myoglobin : num -1.139 -1.661 0.182 -1.897 -1.204 ...
## $ NT_proBNP : num 4.11 4.52 4.44 4.61 4.51 ...
## $ NrCAM : num 4.97 4.23 3.71 4.75 4.3 ...
## $ Osteopontin : num 5.77 5.28 4.96 5.23 4.79 ...
## $ PAI_1 : num 0 -0.0957 0.4905 0.4905 -0.0957 ...
## $ PAPP_A : num -2.79 -3.03 -2.71 -3.15 -3.08 ...
## $ PLGF : num 3.43 3.69 4.08 3.85 3.76 ...
## $ PYY : num 2.83 3 2.94 2.89 2.83 ...
## $ Pancreatic_polypeptide : num -0.821 0.262 -0.105 0.262 0.182 ...
## $ Prolactin : num -0.0408 0.7419 0.3365 0 -0.2485 ...
## $ Prostatic_Acid_Phosphatase : num -1.74 -1.68 -1.68 -1.69 -1.69 ...
## $ Protein_S : num -2.7 -2.36 -2.36 -2.7 -2.58 ...
## $ Pulmonary_and_Activation_Regulat: num -1.11 -1.31 -1.97 -1.97 -1.39 ...
## $ RANTES : num -5.99 -6.73 -6.65 -6.57 -6.57 ...
## $ Resistin : num -13.5 -15.6 -18 -16 -24.4 ...
## [list output truncated]summary(Alzheimer_Test)## ACE_CD143_Angiotensin_Converti ACTH_Adrenocorticotropic_Hormon
## Min. :-0.5473 Min. :-2.2073
## 1st Qu.: 0.9462 1st Qu.:-1.7148
## Median : 1.3013 Median :-1.5374
## Mean : 1.3105 Mean :-1.5311
## 3rd Qu.: 1.6320 3rd Qu.:-1.3863
## Max. : 3.0890 Max. :-0.7985
## AXL Adiponectin Alpha_1_Antichymotrypsin
## Min. :-0.73509 Min. :-7.059 Min. :0.1823
## 1st Qu.:-0.08175 1st Qu.:-5.737 1st Qu.:1.0647
## Median : 0.28035 Median :-5.360 Median :1.3083
## Mean : 0.25373 Mean :-5.298 Mean :1.3077
## 3rd Qu.: 0.60768 3rd Qu.:-4.917 3rd Qu.:1.5686
## Max. : 1.28634 Max. :-3.474 Max. :2.2192
## Alpha_1_Antitrypsin Alpha_1_Microglobulin Alpha_2_Macroglobulin
## Min. :-18.17 Min. :-4.135 Min. :-238.64
## 1st Qu.:-14.70 1st Qu.:-3.284 1st Qu.:-186.64
## Median :-13.59 Median :-3.006 Median :-162.93
## Mean :-13.49 Mean :-2.983 Mean :-162.89
## 3rd Qu.:-12.31 3rd Qu.:-2.674 3rd Qu.:-136.53
## Max. :-10.06 Max. :-1.897 Max. : -50.17
## Angiopoietin_2_ANG_2 Angiotensinogen Apolipoprotein_A_IV Apolipoprotein_A1
## Min. :-0.05129 Min. :1.710 Min. :-2.749 Min. :-8.568
## 1st Qu.: 0.35372 1st Qu.:2.068 1st Qu.:-2.186 1st Qu.:-7.818
## Median : 0.55921 Median :2.276 Median :-1.897 Median :-7.497
## Mean : 0.60278 Mean :2.274 Mean :-1.867 Mean :-7.488
## 3rd Qu.: 0.78846 3rd Qu.:2.430 3rd Qu.:-1.526 3rd Qu.:-7.176
## Max. : 1.77495 Max. :2.752 Max. :-1.109 Max. :-6.645
## Apolipoprotein_A2 Apolipoprotein_B Apolipoprotein_CI Apolipoprotein_CIII
## Min. :-1.9661 Min. :-8.192 Min. :-2.847 Min. :-3.863
## 1st Qu.:-0.9416 1st Qu.:-6.748 1st Qu.:-1.897 1st Qu.:-2.781
## Median :-0.7032 Median :-5.819 Median :-1.609 Median :-2.557
## Mean :-0.6902 Mean :-5.649 Mean :-1.625 Mean :-2.523
## 3rd Qu.:-0.3533 3rd Qu.:-4.603 3rd Qu.:-1.309 3rd Qu.:-2.231
## Max. : 0.5306 Max. :-2.339 Max. :-0.462 Max. :-1.386
## Apolipoprotein_D Apolipoprotein_E Apolipoprotein_H
## Min. :0.2624 Min. :0.6626 Min. :-1.1609
## 1st Qu.:1.1314 1st Qu.:2.1526 1st Qu.:-0.5317
## Median :1.3863 Median :2.8181 Median :-0.2897
## Mean :1.3943 Mean :2.7160 Mean :-0.3212
## 3rd Qu.:1.6864 3rd Qu.:3.2363 3rd Qu.:-0.1032
## Max. :2.6391 Max. :4.6844 Max. : 0.4402
## B_Lymphocyte_Chemoattractant_BL BMP_6 Beta_2_Microglobulin
## Min. :0.7318 Min. :-2.669 Min. :-0.51083
## 1st Qu.:1.5304 1st Qu.:-2.152 1st Qu.:-0.06188
## Median :1.8528 Median :-1.964 Median : 0.18232
## Mean :1.8766 Mean :-1.937 Mean : 0.15566
## 3rd Qu.:2.3714 3rd Qu.:-1.675 3rd Qu.: 0.40547
## Max. :2.9757 Max. :-1.181 Max. : 0.83291
## Betacellulin C_Reactive_Protein CD40 CD5L
## Min. :32.00 Min. :-8.112 Min. :-1.9390 Min. :-1.96611
## 1st Qu.:46.00 1st Qu.:-6.725 1st Qu.:-1.4420 1st Qu.:-0.36747
## Median :51.00 Median :-6.166 Median :-1.2574 Median :-0.05135
## Mean :52.74 Mean :-5.997 Mean :-1.2773 Mean :-0.08760
## 3rd Qu.:59.75 3rd Qu.:-5.369 3rd Qu.:-1.1034 3rd Qu.: 0.24235
## Max. :80.00 Max. :-3.411 Max. :-0.7766 Max. : 0.91629
## Calbindin Calcitonin CgA Clusterin_Apo_J
## Min. :10.81 Min. :-0.7134 Min. :166.6 Min. :1.932
## 1st Qu.:18.88 1st Qu.: 1.2014 1st Qu.:268.2 1st Qu.:2.565
## Median :21.06 Median : 1.6849 Median :324.7 Median :2.833
## Mean :21.49 Mean : 1.7250 Mean :320.2 Mean :2.845
## 3rd Qu.:24.00 3rd Qu.: 2.2618 3rd Qu.:362.3 3rd Qu.:3.045
## Max. :35.36 Max. : 4.1109 Max. :494.5 Max. :3.761
## Complement_3 Complement_Factor_H Connective_Tissue_Growth_Factor
## Min. :-22.40 Min. :0.2766 Min. :0.09531
## 1st Qu.:-17.50 1st Qu.:2.6019 1st Qu.:0.58779
## Median :-15.90 Median :3.3983 Median :0.74194
## Mean :-15.91 Mean :3.3897 Mean :0.74507
## 3rd Qu.:-14.34 3rd Qu.:4.2548 3rd Qu.:0.87547
## Max. :-10.23 Max. :6.5597 Max. :1.41099
## Cortisol Creatine_Kinase_MB Cystatin_C EGF_R
## Min. : 0.10 Min. :-1.872 Min. :7.728 Min. :-1.2694
## 1st Qu.: 8.90 1st Qu.:-1.721 1st Qu.:8.301 1st Qu.:-0.8859
## Median :10.00 Median :-1.651 Median :8.544 Median :-0.6917
## Mean :10.46 Mean :-1.652 Mean :8.576 Mean :-0.6965
## 3rd Qu.:12.00 3rd Qu.:-1.590 3rd Qu.:8.837 3rd Qu.:-0.5034
## Max. :22.00 Max. :-1.434 Max. :9.694 Max. : 0.1891
## EN_RAGE ENA_78 Eotaxin_3 FAS
## Min. :-8.3774 Min. :-1.405 Min. : 23.00 Min. :-1.1087
## 1st Qu.:-4.1836 1st Qu.:-1.382 1st Qu.: 43.00 1st Qu.:-0.7133
## Median :-3.6889 Median :-1.374 Median : 54.00 Median :-0.5798
## Mean :-3.5986 Mean :-1.376 Mean : 55.55 Mean :-0.5414
## 3rd Qu.:-3.2189 3rd Qu.:-1.368 3rd Qu.: 64.00 3rd Qu.:-0.3355
## Max. :-0.8675 Max. :-1.353 Max. :107.00 Max. : 0.1823
## FSH_Follicle_Stimulation_Hormon Fas_Ligand Fatty_Acid_Binding_Protein
## Min. :-1.8101 Min. :0.288 Min. :-0.4559
## 1st Qu.:-1.2694 1st Qu.:2.073 1st Qu.: 0.7998
## Median :-0.9763 Median :2.665 Median : 1.1866
## Mean :-1.0597 Mean :2.649 Mean : 1.2884
## 3rd Qu.:-0.8068 3rd Qu.:3.162 3rd Qu.: 1.9192
## Max. :-0.4757 Max. :5.377 Max. : 3.2188
## Ferritin Fetuin_A Fibrinogen GRO_alpha
## Min. :0.8983 Min. :0.5306 Min. :-9.373 Min. :1.271
## 1st Qu.:2.1473 1st Qu.:1.0296 1st Qu.:-7.799 1st Qu.:1.351
## Median :2.6260 Median :1.3083 Median :-7.316 Median :1.372
## Mean :2.7069 Mean :1.3116 Mean :-7.360 Mean :1.378
## 3rd Qu.:3.1672 3rd Qu.:1.6094 3rd Qu.:-6.970 3rd Qu.:1.398
## Max. :4.9282 Max. :2.2083 Max. :-6.166 Max. :1.514
## Gamma_Interferon_induced_Monokin Glutathione_S_Transferase_alpha
## Min. :2.545 Min. :0.5661
## 1st Qu.:2.698 1st Qu.:0.8257
## Median :2.768 Median :0.9493
## Mean :2.772 Mean :0.9440
## 3rd Qu.:2.829 3rd Qu.:1.0457
## Max. :3.046 Max. :1.3102
## HB_EGF HCC_4 Hepatocyte_Growth_Factor_HGF I_309
## Min. : 3.521 Min. :-4.343 Min. :-0.61619 Min. :2.041
## 1st Qu.: 5.949 1st Qu.:-3.772 1st Qu.:-0.05661 1st Qu.:2.724
## Median : 6.980 Median :-3.540 Median : 0.18232 Median :2.944
## Mean : 6.844 Mean :-3.538 Mean : 0.18076 Mean :2.921
## 3rd Qu.: 7.745 3rd Qu.:-3.352 3rd Qu.: 0.33647 3rd Qu.:3.135
## Max. :10.359 Max. :-2.489 Max. : 1.09861 Max. :3.689
## ICAM_1 IGF_BP_2 IL_11 IL_13
## Min. :-1.4661 Min. :4.718 Min. :2.031 Min. :1.232
## 1st Qu.:-0.7671 1st Qu.:5.127 1st Qu.:3.960 1st Qu.:1.274
## Median :-0.5903 Median :5.255 Median :4.838 Median :1.283
## Mean :-0.5958 Mean :5.263 Mean :4.651 Mean :1.283
## 3rd Qu.:-0.3574 3rd Qu.:5.402 3rd Qu.:5.482 3rd Qu.:1.292
## Max. : 0.3602 Max. :5.916 Max. :8.692 Max. :1.310
## IL_16 IL_17E IL_1alpha IL_3
## Min. :0.9568 Min. :1.582 Min. :-8.468 Min. :-5.521
## 1st Qu.:2.4411 1st Qu.:3.637 1st Qu.:-7.849 1st Qu.:-4.324
## Median :2.8763 Median :4.723 Median :-7.562 Median :-3.963
## Mean :2.8176 Mean :4.774 Mean :-7.549 Mean :-3.976
## 3rd Qu.:3.3514 3rd Qu.:5.415 3rd Qu.:-7.279 3rd Qu.:-3.576
## Max. :4.1028 Max. :8.081 Max. :-6.377 Max. :-3.079
## IL_4 IL_5 IL_6 IL_6_Receptor
## Min. :0.5306 Min. :-1.04982 Min. :-1.53428 Min. :-0.74560
## 1st Qu.:1.4586 1st Qu.:-0.03062 1st Qu.:-0.40924 1st Qu.:-0.20131
## Median :1.7226 Median : 0.22234 Median :-0.07205 Median : 0.00000
## Mean :1.7445 Mean : 0.22853 Mean :-0.05216 Mean : 0.06213
## 3rd Qu.:2.0669 3rd Qu.: 0.53063 3rd Qu.: 0.34805 3rd Qu.: 0.27297
## Max. :2.7081 Max. : 1.13140 Max. : 1.00562 Max. : 0.77048
## IL_7 IL_8 IP_10_Inducible_Protein_10 IgA
## Min. :1.310 Min. :1.615 Min. :4.263 Min. :-7.621
## 1st Qu.:2.379 1st Qu.:1.684 1st Qu.:5.323 1st Qu.:-6.571
## Median :3.148 Median :1.702 Median :5.617 Median :-6.012
## Mean :3.143 Mean :1.704 Mean :5.636 Mean :-6.066
## 3rd Qu.:3.706 3rd Qu.:1.725 3rd Qu.:5.917 3rd Qu.:-5.606
## Max. :5.000 Max. :1.836 Max. :7.208 Max. :-4.733
## Insulin Kidney_Injury_Molecule_1_KIM_1 LOX_1
## Min. :-2.0099 Min. :-1.251 Min. :0.0000
## 1st Qu.:-1.4466 1st Qu.:-1.209 1st Qu.:0.9649
## Median :-1.2169 Median :-1.187 Median :1.2238
## Mean :-1.1998 Mean :-1.188 Mean :1.2085
## 3rd Qu.:-1.0105 3rd Qu.:-1.166 3rd Qu.:1.4351
## Max. :-0.5025 Max. :-1.124 Max. :2.3979
## Leptin Lipoprotein_a MCP_1 MCP_2
## Min. :-1.9471 Min. :-6.571 Min. :5.889 Min. :0.4006
## 1st Qu.:-1.6334 1st Qu.:-5.116 1st Qu.:6.318 1st Qu.:1.5304
## Median :-1.4294 Median :-4.657 Median :6.482 Median :1.8528
## Mean :-1.4363 Mean :-4.515 Mean :6.480 Mean :1.8104
## 3rd Qu.:-1.2409 3rd Qu.:-4.017 3rd Qu.:6.627 3rd Qu.:2.0827
## Max. :-0.8387 Max. :-2.040 Max. :7.065 Max. :3.7545
## MIF MIP_1alpha MIP_1beta MMP_2
## Min. :-2.797 Min. :1.008 Min. :1.917 Min. :0.6248
## 1st Qu.:-2.120 1st Qu.:3.302 1st Qu.:2.485 1st Qu.:2.5513
## Median :-1.966 Median :3.736 Median :2.773 Median :2.9937
## Mean :-1.932 Mean :3.898 Mean :2.784 Mean :3.0347
## 3rd Qu.:-1.715 3rd Qu.:4.686 3rd Qu.:3.079 3rd Qu.:3.4798
## Max. :-1.109 Max. :5.735 Max. :3.784 Max. :6.0996
## MMP_3 MMP10 MMP7 Myoglobin
## Min. :-3.650 Min. :-4.948 Min. :-7.5346 Min. :-3.2968
## 1st Qu.:-2.852 1st Qu.:-4.075 1st Qu.:-4.9634 1st Qu.:-2.0217
## Median :-2.532 Median :-3.612 Median :-4.0302 Median :-1.5874
## Mean :-2.490 Mean :-3.676 Mean :-4.0148 Mean :-1.4165
## 3rd Qu.:-2.120 3rd Qu.:-3.331 3rd Qu.:-3.1640 3rd Qu.:-0.7765
## Max. :-1.171 Max. :-2.900 Max. :-0.1953 Max. : 1.1314
## NT_proBNP NrCAM Osteopontin PAI_1
## Min. :3.611 Min. :2.890 Min. :4.078 Min. :-0.990849
## 1st Qu.:4.174 1st Qu.:3.871 1st Qu.:4.892 1st Qu.:-0.334043
## Median :4.477 Median :4.317 Median :5.168 Median : 0.000000
## Mean :4.488 Mean :4.291 Mean :5.177 Mean :-0.003947
## 3rd Qu.:4.794 3rd Qu.:4.725 3rd Qu.:5.410 3rd Qu.: 0.303112
## Max. :5.398 Max. :5.690 Max. :6.315 Max. : 0.885785
## PAPP_A PLGF PYY Pancreatic_polypeptide
## Min. :-3.152 Min. :2.639 Min. :2.398 Min. :-1.609438
## 1st Qu.:-2.971 1st Qu.:3.689 1st Qu.:2.833 1st Qu.:-0.506693
## Median :-2.841 Median :3.892 Median :2.996 Median : 0.138816
## Mean :-2.845 Mean :3.884 Mean :2.976 Mean :-0.005258
## 3rd Qu.:-2.719 3rd Qu.:4.123 3rd Qu.:3.178 3rd Qu.: 0.470004
## Max. :-2.488 Max. :4.710 Max. :3.738 Max. : 1.504077
## Prolactin Prostatic_Acid_Phosphatase Protein_S
## Min. :-0.38566 Min. :-1.800 Min. :-3.154
## 1st Qu.:-0.16558 1st Qu.:-1.739 1st Qu.:-2.579
## Median : 0.00000 Median :-1.690 Median :-2.259
## Mean : 0.05195 Mean :-1.692 Mean :-2.268
## 3rd Qu.: 0.18232 3rd Qu.:-1.659 3rd Qu.:-1.924
## Max. : 0.78846 Max. :-1.540 Max. :-1.547
## Pulmonary_and_Activation_Regulat RANTES Resistin
## Min. :-2.4418 Min. :-7.236 Min. :-30.156
## 1st Qu.:-1.8326 1st Qu.:-6.725 1st Qu.:-22.131
## Median :-1.5141 Median :-6.571 Median :-18.014
## Mean :-1.5007 Mean :-6.540 Mean :-18.245
## 3rd Qu.:-1.1712 3rd Qu.:-6.392 3rd Qu.:-15.202
## Max. :-0.4463 Max. :-5.843 Max. : -6.594
## S100b SGOT SHBG SOD
## Min. :0.1874 Min. :-1.8971 Min. :-3.730 Min. :4.382
## 1st Qu.:0.9600 1st Qu.:-0.7498 1st Qu.:-3.052 1st Qu.:5.006
## Median :1.1571 Median :-0.4780 Median :-2.711 Median :5.313
## Mean :1.1819 Mean :-0.4898 Mean :-2.686 Mean :5.302
## 3rd Qu.:1.3807 3rd Qu.:-0.2138 3rd Qu.:-2.343 3rd Qu.:5.547
## Max. :2.1950 Max. : 0.1823 Max. :-1.561 Max. :6.461
## Serum_Amyloid_P Sortilin Stem_Cell_Factor TGF_alpha
## Min. :-7.182 Min. :1.508 Min. :2.219 Min. : 7.500
## 1st Qu.:-6.438 1st Qu.:3.177 1st Qu.:3.045 1st Qu.: 9.062
## Median :-6.215 Median :3.867 Median :3.314 Median : 9.596
## Mean :-6.083 Mean :3.787 Mean :3.267 Mean : 9.776
## 3rd Qu.:-5.607 3rd Qu.:4.371 3rd Qu.:3.466 3rd Qu.:10.612
## Max. :-4.699 Max. :5.681 Max. :4.078 Max. :13.083
## TIMP_1 TNF_RII TRAIL_R3 TTR_prealbumin
## Min. : 8.198 Min. :-1.6607 Min. :-1.30636 Min. :2.485
## 1st Qu.:10.530 1st Qu.:-0.8675 1st Qu.:-0.73332 1st Qu.:2.773
## Median :11.341 Median :-0.6541 Median :-0.55547 Median :2.890
## Mean :11.520 Mean :-0.6270 Mean :-0.58640 Mean :2.854
## 3rd Qu.:12.352 3rd Qu.:-0.3320 3rd Qu.:-0.47065 3rd Qu.:2.944
## Max. :16.547 Max. : 0.4055 Max. : 0.09622 Max. :3.091
## Tamm_Horsfall_Protein_THP Thrombomodulin Thrombopoietin
## Min. :-3.206 Min. :-2.054 Min. :-1.5396
## 1st Qu.:-3.144 1st Qu.:-1.675 1st Qu.:-0.8383
## Median :-3.126 Median :-1.534 Median :-0.7039
## Mean :-3.123 Mean :-1.533 Mean :-0.7192
## 3rd Qu.:-3.101 3rd Qu.:-1.341 3rd Qu.:-0.6289
## Max. :-3.041 Max. :-1.019 Max. :-0.3029
## Thymus_Expressed_Chemokine_TECK Thyroid_Stimulating_Hormone
## Min. :2.141 Min. :-6.190
## 1st Qu.:3.283 1st Qu.:-4.733
## Median :3.753 Median :-4.269
## Mean :3.770 Mean :-4.221
## 3rd Qu.:4.316 3rd Qu.:-3.828
## Max. :5.681 Max. :-2.040
## Thyroxine_Binding_Globulin Tissue_Factor Transferrin
## Min. :-2.3026 Min. :0.0000 Min. :2.282
## 1st Qu.:-1.7148 1st Qu.:0.7053 1st Qu.:2.708
## Median :-1.4919 Median :1.1473 Median :2.890
## Mean :-1.4902 Mean :1.1356 Mean :2.900
## 3rd Qu.:-1.2379 3rd Qu.:1.5149 3rd Qu.:3.135
## Max. :-0.5978 Max. :2.7081 Max. :3.497
## Trefoil_Factor_3_TFF3 VCAM_1 VEGF Vitronectin
## Min. :-4.906 Min. :2.028 Min. :12.23 Min. :-1.07881
## 1st Qu.:-4.200 1st Qu.:2.420 1st Qu.:15.03 1st Qu.:-0.46204
## Median :-3.912 Median :2.674 Median :17.08 Median :-0.28106
## Mean :-3.947 Mean :2.644 Mean :16.70 Mean :-0.26833
## 3rd Qu.:-3.772 3rd Qu.:2.833 3rd Qu.:18.19 3rd Qu.:-0.05394
## Max. :-3.170 Max. :3.466 Max. :21.18 Max. : 0.40547
## von_Willebrand_Factor Class E4 E3
## Min. :-4.920 Impaired:18 Min. :0.000 Min. :0.0000
## 1st Qu.:-4.269 Control :48 1st Qu.:0.000 1st Qu.:1.0000
## Median :-4.017 Median :0.000 Median :1.0000
## Mean :-4.014 Mean :0.303 Mean :0.9848
## 3rd Qu.:-3.730 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :-3.058 Max. :1.000 Max. :1.0000
## E2
## Min. :0.00000
## 1st Qu.:0.00000
## Median :0.00000
## Mean :0.06061
## 3rd Qu.:0.00000
## Max. :1.00000##################################
# Formulating a data type assessment summary
##################################
PDA <- Alzheimer_Train
(PDA.Summary <- data.frame(
Column.Index=c(1:length(names(PDA))),
Column.Name= names(PDA),
Column.Type=sapply(PDA, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 ACE_CD143_Angiotensin_Converti numeric
## 2 2 ACTH_Adrenocorticotropic_Hormon numeric
## 3 3 AXL numeric
## 4 4 Adiponectin numeric
## 5 5 Alpha_1_Antichymotrypsin numeric
## 6 6 Alpha_1_Antitrypsin numeric
## 7 7 Alpha_1_Microglobulin numeric
## 8 8 Alpha_2_Macroglobulin numeric
## 9 9 Angiopoietin_2_ANG_2 numeric
## 10 10 Angiotensinogen numeric
## 11 11 Apolipoprotein_A_IV numeric
## 12 12 Apolipoprotein_A1 numeric
## 13 13 Apolipoprotein_A2 numeric
## 14 14 Apolipoprotein_B numeric
## 15 15 Apolipoprotein_CI numeric
## 16 16 Apolipoprotein_CIII numeric
## 17 17 Apolipoprotein_D numeric
## 18 18 Apolipoprotein_E numeric
## 19 19 Apolipoprotein_H numeric
## 20 20 B_Lymphocyte_Chemoattractant_BL numeric
## 21 21 BMP_6 numeric
## 22 22 Beta_2_Microglobulin numeric
## 23 23 Betacellulin integer
## 24 24 C_Reactive_Protein numeric
## 25 25 CD40 numeric
## 26 26 CD5L numeric
## 27 27 Calbindin numeric
## 28 28 Calcitonin numeric
## 29 29 CgA numeric
## 30 30 Clusterin_Apo_J numeric
## 31 31 Complement_3 numeric
## 32 32 Complement_Factor_H numeric
## 33 33 Connective_Tissue_Growth_Factor numeric
## 34 34 Cortisol numeric
## 35 35 Creatine_Kinase_MB numeric
## 36 36 Cystatin_C numeric
## 37 37 EGF_R numeric
## 38 38 EN_RAGE numeric
## 39 39 ENA_78 numeric
## 40 40 Eotaxin_3 integer
## 41 41 FAS numeric
## 42 42 FSH_Follicle_Stimulation_Hormon numeric
## 43 43 Fas_Ligand numeric
## 44 44 Fatty_Acid_Binding_Protein numeric
## 45 45 Ferritin numeric
## 46 46 Fetuin_A numeric
## 47 47 Fibrinogen numeric
## 48 48 GRO_alpha numeric
## 49 49 Gamma_Interferon_induced_Monokin numeric
## 50 50 Glutathione_S_Transferase_alpha numeric
## 51 51 HB_EGF numeric
## 52 52 HCC_4 numeric
## 53 53 Hepatocyte_Growth_Factor_HGF numeric
## 54 54 I_309 numeric
## 55 55 ICAM_1 numeric
## 56 56 IGF_BP_2 numeric
## 57 57 IL_11 numeric
## 58 58 IL_13 numeric
## 59 59 IL_16 numeric
## 60 60 IL_17E numeric
## 61 61 IL_1alpha numeric
## 62 62 IL_3 numeric
## 63 63 IL_4 numeric
## 64 64 IL_5 numeric
## 65 65 IL_6 numeric
## 66 66 IL_6_Receptor numeric
## 67 67 IL_7 numeric
## 68 68 IL_8 numeric
## 69 69 IP_10_Inducible_Protein_10 numeric
## 70 70 IgA numeric
## 71 71 Insulin numeric
## 72 72 Kidney_Injury_Molecule_1_KIM_1 numeric
## 73 73 LOX_1 numeric
## 74 74 Leptin numeric
## 75 75 Lipoprotein_a numeric
## 76 76 MCP_1 numeric
## 77 77 MCP_2 numeric
## 78 78 MIF numeric
## 79 79 MIP_1alpha numeric
## 80 80 MIP_1beta numeric
## 81 81 MMP_2 numeric
## 82 82 MMP_3 numeric
## 83 83 MMP10 numeric
## 84 84 MMP7 numeric
## 85 85 Myoglobin numeric
## 86 86 NT_proBNP numeric
## 87 87 NrCAM numeric
## 88 88 Osteopontin numeric
## 89 89 PAI_1 numeric
## 90 90 PAPP_A numeric
## 91 91 PLGF numeric
## 92 92 PYY numeric
## 93 93 Pancreatic_polypeptide numeric
## 94 94 Prolactin numeric
## 95 95 Prostatic_Acid_Phosphatase numeric
## 96 96 Protein_S numeric
## 97 97 Pulmonary_and_Activation_Regulat numeric
## 98 98 RANTES numeric
## 99 99 Resistin numeric
## 100 100 S100b numeric
## 101 101 SGOT numeric
## 102 102 SHBG numeric
## 103 103 SOD numeric
## 104 104 Serum_Amyloid_P numeric
## 105 105 Sortilin numeric
## 106 106 Stem_Cell_Factor numeric
## 107 107 TGF_alpha numeric
## 108 108 TIMP_1 numeric
## 109 109 TNF_RII numeric
## 110 110 TRAIL_R3 numeric
## 111 111 TTR_prealbumin numeric
## 112 112 Tamm_Horsfall_Protein_THP numeric
## 113 113 Thrombomodulin numeric
## 114 114 Thrombopoietin numeric
## 115 115 Thymus_Expressed_Chemokine_TECK numeric
## 116 116 Thyroid_Stimulating_Hormone numeric
## 117 117 Thyroxine_Binding_Globulin numeric
## 118 118 Tissue_Factor numeric
## 119 119 Transferrin numeric
## 120 120 Trefoil_Factor_3_TFF3 numeric
## 121 121 VCAM_1 numeric
## 122 122 VEGF numeric
## 123 123 Vitronectin numeric
## 124 124 von_Willebrand_Factor numeric
## 125 125 Class factor
## 126 126 E4 numeric
## 127 127 E3 numeric
## 128 128 E2 numeric1.2 Data Quality Assessment
Data quality assessment:
[A] No missing observations noted for any variable.
[B] Low variance observed for 2 variables with First.Second.Mode.Ratio>5.
[B.1] E2 variable (factor)
[B.2] E3 variable (factor)
[C] No low variance observed for any variable with Unique.Count.Ratio<0.01.
[D] No high skewness observed for any variable with Skewness>3 or Skewness<(-3).
##################################
# Loading dataset
##################################
DQA <- Alzheimer_Train
##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
Column.Index=c(1:length(names(DQA))),
Column.Name= names(DQA),
Column.Type=sapply(DQA, function(x) class(x)),
Row.Count=sapply(DQA, function(x) nrow(DQA)),
NA.Count=sapply(DQA,function(x)sum(is.na(x))),
Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
row.names=NULL)
)## Column.Index Column.Name Column.Type Row.Count
## 1 1 ACE_CD143_Angiotensin_Converti numeric 267
## 2 2 ACTH_Adrenocorticotropic_Hormon numeric 267
## 3 3 AXL numeric 267
## 4 4 Adiponectin numeric 267
## 5 5 Alpha_1_Antichymotrypsin numeric 267
## 6 6 Alpha_1_Antitrypsin numeric 267
## 7 7 Alpha_1_Microglobulin numeric 267
## 8 8 Alpha_2_Macroglobulin numeric 267
## 9 9 Angiopoietin_2_ANG_2 numeric 267
## 10 10 Angiotensinogen numeric 267
## 11 11 Apolipoprotein_A_IV numeric 267
## 12 12 Apolipoprotein_A1 numeric 267
## 13 13 Apolipoprotein_A2 numeric 267
## 14 14 Apolipoprotein_B numeric 267
## 15 15 Apolipoprotein_CI numeric 267
## 16 16 Apolipoprotein_CIII numeric 267
## 17 17 Apolipoprotein_D numeric 267
## 18 18 Apolipoprotein_E numeric 267
## 19 19 Apolipoprotein_H numeric 267
## 20 20 B_Lymphocyte_Chemoattractant_BL numeric 267
## 21 21 BMP_6 numeric 267
## 22 22 Beta_2_Microglobulin numeric 267
## 23 23 Betacellulin integer 267
## 24 24 C_Reactive_Protein numeric 267
## 25 25 CD40 numeric 267
## 26 26 CD5L numeric 267
## 27 27 Calbindin numeric 267
## 28 28 Calcitonin numeric 267
## 29 29 CgA numeric 267
## 30 30 Clusterin_Apo_J numeric 267
## 31 31 Complement_3 numeric 267
## 32 32 Complement_Factor_H numeric 267
## 33 33 Connective_Tissue_Growth_Factor numeric 267
## 34 34 Cortisol numeric 267
## 35 35 Creatine_Kinase_MB numeric 267
## 36 36 Cystatin_C numeric 267
## 37 37 EGF_R numeric 267
## 38 38 EN_RAGE numeric 267
## 39 39 ENA_78 numeric 267
## 40 40 Eotaxin_3 integer 267
## 41 41 FAS numeric 267
## 42 42 FSH_Follicle_Stimulation_Hormon numeric 267
## 43 43 Fas_Ligand numeric 267
## 44 44 Fatty_Acid_Binding_Protein numeric 267
## 45 45 Ferritin numeric 267
## 46 46 Fetuin_A numeric 267
## 47 47 Fibrinogen numeric 267
## 48 48 GRO_alpha numeric 267
## 49 49 Gamma_Interferon_induced_Monokin numeric 267
## 50 50 Glutathione_S_Transferase_alpha numeric 267
## 51 51 HB_EGF numeric 267
## 52 52 HCC_4 numeric 267
## 53 53 Hepatocyte_Growth_Factor_HGF numeric 267
## 54 54 I_309 numeric 267
## 55 55 ICAM_1 numeric 267
## 56 56 IGF_BP_2 numeric 267
## 57 57 IL_11 numeric 267
## 58 58 IL_13 numeric 267
## 59 59 IL_16 numeric 267
## 60 60 IL_17E numeric 267
## 61 61 IL_1alpha numeric 267
## 62 62 IL_3 numeric 267
## 63 63 IL_4 numeric 267
## 64 64 IL_5 numeric 267
## 65 65 IL_6 numeric 267
## 66 66 IL_6_Receptor numeric 267
## 67 67 IL_7 numeric 267
## 68 68 IL_8 numeric 267
## 69 69 IP_10_Inducible_Protein_10 numeric 267
## 70 70 IgA numeric 267
## 71 71 Insulin numeric 267
## 72 72 Kidney_Injury_Molecule_1_KIM_1 numeric 267
## 73 73 LOX_1 numeric 267
## 74 74 Leptin numeric 267
## 75 75 Lipoprotein_a numeric 267
## 76 76 MCP_1 numeric 267
## 77 77 MCP_2 numeric 267
## 78 78 MIF numeric 267
## 79 79 MIP_1alpha numeric 267
## 80 80 MIP_1beta numeric 267
## 81 81 MMP_2 numeric 267
## 82 82 MMP_3 numeric 267
## 83 83 MMP10 numeric 267
## 84 84 MMP7 numeric 267
## 85 85 Myoglobin numeric 267
## 86 86 NT_proBNP numeric 267
## 87 87 NrCAM numeric 267
## 88 88 Osteopontin numeric 267
## 89 89 PAI_1 numeric 267
## 90 90 PAPP_A numeric 267
## 91 91 PLGF numeric 267
## 92 92 PYY numeric 267
## 93 93 Pancreatic_polypeptide numeric 267
## 94 94 Prolactin numeric 267
## 95 95 Prostatic_Acid_Phosphatase numeric 267
## 96 96 Protein_S numeric 267
## 97 97 Pulmonary_and_Activation_Regulat numeric 267
## 98 98 RANTES numeric 267
## 99 99 Resistin numeric 267
## 100 100 S100b numeric 267
## 101 101 SGOT numeric 267
## 102 102 SHBG numeric 267
## 103 103 SOD numeric 267
## 104 104 Serum_Amyloid_P numeric 267
## 105 105 Sortilin numeric 267
## 106 106 Stem_Cell_Factor numeric 267
## 107 107 TGF_alpha numeric 267
## 108 108 TIMP_1 numeric 267
## 109 109 TNF_RII numeric 267
## 110 110 TRAIL_R3 numeric 267
## 111 111 TTR_prealbumin numeric 267
## 112 112 Tamm_Horsfall_Protein_THP numeric 267
## 113 113 Thrombomodulin numeric 267
## 114 114 Thrombopoietin numeric 267
## 115 115 Thymus_Expressed_Chemokine_TECK numeric 267
## 116 116 Thyroid_Stimulating_Hormone numeric 267
## 117 117 Thyroxine_Binding_Globulin numeric 267
## 118 118 Tissue_Factor numeric 267
## 119 119 Transferrin numeric 267
## 120 120 Trefoil_Factor_3_TFF3 numeric 267
## 121 121 VCAM_1 numeric 267
## 122 122 VEGF numeric 267
## 123 123 Vitronectin numeric 267
## 124 124 von_Willebrand_Factor numeric 267
## 125 125 Class factor 267
## 126 126 E4 numeric 267
## 127 127 E3 numeric 267
## 128 128 E2 numeric 267
## NA.Count Fill.Rate
## 1 0 1.000
## 2 0 1.000
## 3 0 1.000
## 4 0 1.000
## 5 0 1.000
## 6 0 1.000
## 7 0 1.000
## 8 0 1.000
## 9 0 1.000
## 10 0 1.000
## 11 0 1.000
## 12 0 1.000
## 13 0 1.000
## 14 0 1.000
## 15 0 1.000
## 16 0 1.000
## 17 0 1.000
## 18 0 1.000
## 19 0 1.000
## 20 0 1.000
## 21 0 1.000
## 22 0 1.000
## 23 0 1.000
## 24 0 1.000
## 25 0 1.000
## 26 0 1.000
## 27 0 1.000
## 28 0 1.000
## 29 0 1.000
## 30 0 1.000
## 31 0 1.000
## 32 0 1.000
## 33 0 1.000
## 34 0 1.000
## 35 0 1.000
## 36 0 1.000
## 37 0 1.000
## 38 0 1.000
## 39 0 1.000
## 40 0 1.000
## 41 0 1.000
## 42 0 1.000
## 43 0 1.000
## 44 0 1.000
## 45 0 1.000
## 46 0 1.000
## 47 0 1.000
## 48 0 1.000
## 49 0 1.000
## 50 0 1.000
## 51 0 1.000
## 52 0 1.000
## 53 0 1.000
## 54 0 1.000
## 55 0 1.000
## 56 0 1.000
## 57 0 1.000
## 58 0 1.000
## 59 0 1.000
## 60 0 1.000
## 61 0 1.000
## 62 0 1.000
## 63 0 1.000
## 64 0 1.000
## 65 0 1.000
## 66 0 1.000
## 67 0 1.000
## 68 0 1.000
## 69 0 1.000
## 70 0 1.000
## 71 0 1.000
## 72 0 1.000
## 73 0 1.000
## 74 0 1.000
## 75 0 1.000
## 76 0 1.000
## 77 0 1.000
## 78 0 1.000
## 79 0 1.000
## 80 0 1.000
## 81 0 1.000
## 82 0 1.000
## 83 0 1.000
## 84 0 1.000
## 85 0 1.000
## 86 0 1.000
## 87 0 1.000
## 88 0 1.000
## 89 0 1.000
## 90 0 1.000
## 91 0 1.000
## 92 0 1.000
## 93 0 1.000
## 94 0 1.000
## 95 0 1.000
## 96 0 1.000
## 97 0 1.000
## 98 0 1.000
## 99 0 1.000
## 100 0 1.000
## 101 0 1.000
## 102 0 1.000
## 103 0 1.000
## 104 0 1.000
## 105 0 1.000
## 106 0 1.000
## 107 0 1.000
## 108 0 1.000
## 109 0 1.000
## 110 0 1.000
## 111 0 1.000
## 112 0 1.000
## 113 0 1.000
## 114 0 1.000
## 115 0 1.000
## 116 0 1.000
## 117 0 1.000
## 118 0 1.000
## 119 0 1.000
## 120 0 1.000
## 121 0 1.000
## 122 0 1.000
## 123 0 1.000
## 124 0 1.000
## 125 0 1.000
## 126 0 1.000
## 127 0 1.000
## 128 0 1.000##################################
# Listing all predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("Class")]
##################################
# Listing all numeric predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,!names(DQA.Predictors) %in% c("E2","E3","E4")]
DQA.Predictors.Numeric <- as.data.frame(sapply(DQA.Predictors.Numeric,function(x) as.numeric(x)))
if (length(names(DQA.Predictors.Numeric))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Numeric))),
" numeric predictor variable(s)."))
} else {
print("There are no numeric predictor variables.")
}## [1] "There are 124 numeric predictor variable(s)."##################################
# Listing all factor predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,names(DQA.Predictors) %in% c("E2","E3","E4")]
DQA.Predictors.Factor <- as.data.frame(sapply(DQA.Predictors.Factor,function(x) as.factor(x)))
if (length(names(DQA.Predictors.Factor))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Factor))),
" factor predictor variable(s)."))
} else {
print("There are no factor predictor variables.")
}## [1] "There are 3 factor predictor variable(s)."##################################
# Formulating a data quality assessment summary for factor predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = x[!(x %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return("x"),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Factor.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Factor),
Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
row.names=NULL)
)
} ## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 E4 character 2 0 1
## 2 E3 character 2 1 0
## 3 E2 character 2 0 1
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1 160 107 0.007 1.495
## 2 245 22 0.007 11.136
## 3 224 43 0.007 5.209##################################
# Formulating a data quality assessment summary for numeric predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = na.omit(x)[!(na.omit(x) %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return(0.00001),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Numeric.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Numeric),
Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count
## 1 ACE_CD143_Angiotensin_Converti numeric 53
## 2 ACTH_Adrenocorticotropic_Hormon numeric 32
## 3 AXL numeric 59
## 4 Adiponectin numeric 91
## 5 Alpha_1_Antichymotrypsin numeric 65
## 6 Alpha_1_Antitrypsin numeric 64
## 7 Alpha_1_Microglobulin numeric 82
## 8 Alpha_2_Macroglobulin numeric 47
## 9 Angiopoietin_2_ANG_2 numeric 36
## 10 Angiotensinogen numeric 135
## 11 Apolipoprotein_A_IV numeric 53
## 12 Apolipoprotein_A1 numeric 83
## 13 Apolipoprotein_A2 numeric 85
## 14 Apolipoprotein_B numeric 83
## 15 Apolipoprotein_CI numeric 49
## 16 Apolipoprotein_CIII numeric 82
## 17 Apolipoprotein_D numeric 63
## 18 Apolipoprotein_E numeric 79
## 19 Apolipoprotein_H numeric 82
## 20 B_Lymphocyte_Chemoattractant_BL numeric 35
## 21 BMP_6 numeric 42
## 22 Beta_2_Microglobulin numeric 50
## 23 Betacellulin numeric 36
## 24 C_Reactive_Protein numeric 135
## 25 CD40 numeric 34
## 26 CD5L numeric 82
## 27 Calbindin numeric 139
## 28 Calcitonin numeric 93
## 29 CgA numeric 105
## 30 Clusterin_Apo_J numeric 31
## 31 Complement_3 numeric 63
## 32 Complement_Factor_H numeric 87
## 33 Connective_Tissue_Growth_Factor numeric 26
## 34 Cortisol numeric 52
## 35 Creatine_Kinase_MB numeric 32
## 36 Cystatin_C numeric 212
## 37 EGF_R numeric 59
## 38 EN_RAGE numeric 95
## 39 ENA_78 numeric 39
## 40 Eotaxin_3 numeric 42
## 41 FAS numeric 52
## 42 FSH_Follicle_Stimulation_Hormon numeric 97
## 43 Fas_Ligand numeric 57
## 44 Fatty_Acid_Binding_Protein numeric 84
## 45 Ferritin numeric 75
## 46 Fetuin_A numeric 67
## 47 Fibrinogen numeric 91
## 48 GRO_alpha numeric 25
## 49 Gamma_Interferon_induced_Monokin numeric 209
## 50 Glutathione_S_Transferase_alpha numeric 42
## 51 HB_EGF numeric 54
## 52 HCC_4 numeric 53
## 53 Hepatocyte_Growth_Factor_HGF numeric 44
## 54 I_309 numeric 45
## 55 ICAM_1 numeric 68
## 56 IGF_BP_2 numeric 126
## 57 IL_11 numeric 75
## 58 IL_13 numeric 18
## 59 IL_16 numeric 55
## 60 IL_17E numeric 44
## 61 IL_1alpha numeric 60
## 62 IL_3 numeric 58
## 63 IL_4 numeric 47
## 64 IL_5 numeric 48
## 65 IL_6 numeric 53
## 66 IL_6_Receptor numeric 53
## 67 IL_7 numeric 46
## 68 IL_8 numeric 56
## 69 IP_10_Inducible_Protein_10 numeric 211
## 70 IgA numeric 94
## 71 Insulin numeric 55
## 72 Kidney_Injury_Molecule_1_KIM_1 numeric 50
## 73 LOX_1 numeric 67
## 74 Leptin numeric 82
## 75 Lipoprotein_a numeric 128
## 76 MCP_1 numeric 219
## 77 MCP_2 numeric 39
## 78 MIF numeric 43
## 79 MIP_1alpha numeric 49
## 80 MIP_1beta numeric 47
## 81 MMP_2 numeric 48
## 82 MMP_3 numeric 80
## 83 MMP10 numeric 56
## 84 MMP7 numeric 89
## 85 Myoglobin numeric 116
## 86 NT_proBNP numeric 111
## 87 NrCAM numeric 126
## 88 Osteopontin numeric 166
## 89 PAI_1 numeric 70
## 90 PAPP_A numeric 34
## 91 PLGF numeric 86
## 92 PYY numeric 34
## 93 Pancreatic_polypeptide numeric 81
## 94 Prolactin numeric 57
## 95 Prostatic_Acid_Phosphatase numeric 43
## 96 Protein_S numeric 24
## 97 Pulmonary_and_Activation_Regulat numeric 54
## 98 RANTES numeric 39
## 99 Resistin numeric 59
## 100 S100b numeric 35
## 101 SGOT numeric 75
## 102 SHBG numeric 92
## 103 SOD numeric 164
## 104 Serum_Amyloid_P numeric 74
## 105 Sortilin numeric 65
## 106 Stem_Cell_Factor numeric 44
## 107 TGF_alpha numeric 62
## 108 TIMP_1 numeric 56
## 109 TNF_RII numeric 72
## 110 TRAIL_R3 numeric 60
## 111 TTR_prealbumin numeric 16
## 112 Tamm_Horsfall_Protein_THP numeric 60
## 113 Thrombomodulin numeric 35
## 114 Thrombopoietin numeric 54
## 115 Thymus_Expressed_Chemokine_TECK numeric 37
## 116 Thyroid_Stimulating_Hormone numeric 66
## 117 Thyroxine_Binding_Globulin numeric 49
## 118 Tissue_Factor numeric 67
## 119 Transferrin numeric 30
## 120 Trefoil_Factor_3_TFF3 numeric 37
## 121 VCAM_1 numeric 40
## 122 VEGF numeric 200
## 123 Vitronectin numeric 71
## 124 von_Willebrand_Factor numeric 43
## Unique.Count.Ratio First.Mode.Value Second.Mode.Value First.Mode.Count
## 1 0.199 1.157 1.107 14
## 2 0.120 -1.609 -1.715 20
## 3 0.221 0.191 0.530 29
## 4 0.341 -5.991 -4.200 7
## 5 0.243 1.194 1.099 12
## 6 0.240 -12.907 -13.310 11
## 7 0.307 -3.244 -3.270 13
## 8 0.176 -179.087 -194.947 21
## 9 0.135 0.531 0.642 23
## 10 0.506 2.262 2.107 9
## 11 0.199 -2.040 -1.772 30
## 12 0.311 -7.902 -7.452 8
## 13 0.318 -0.968 -0.755 9
## 14 0.311 -6.211 -7.289 11
## 15 0.184 -1.715 -1.661 23
## 16 0.307 -2.120 -2.207 17
## 17 0.236 1.308 1.386 11
## 18 0.296 2.720 4.024 11
## 19 0.307 0.097 -0.383 14
## 20 0.131 2.371 1.981 36
## 21 0.157 -1.675 -1.845 30
## 22 0.187 0.095 0.182 38
## 23 0.135 51.000 42.000 50
## 24 0.506 -5.745 -6.571 7
## 25 0.127 -1.242 -1.273 25
## 26 0.307 0.095 0.182 17
## 27 0.521 21.495 20.891 8
## 28 0.348 0.693 0.956 15
## 29 0.393 315.308 361.583 9
## 30 0.116 2.773 2.890 25
## 31 0.236 -16.545 -18.173 15
## 32 0.326 4.024 4.475 22
## 33 0.097 0.693 0.788 25
## 34 0.195 12.000 11.000 38
## 35 0.120 -1.671 -1.724 27
## 36 0.794 8.470 8.357 4
## 37 0.221 -0.590 -0.700 12
## 38 0.356 -4.200 -4.423 11
## 39 0.146 -1.368 -1.364 39
## 40 0.157 64.000 44.000 38
## 41 0.195 -0.713 -0.528 23
## 42 0.363 -1.064 -1.361 10
## 43 0.213 3.101 2.792 19
## 44 0.315 0.624 0.269 9
## 45 0.281 2.382 3.329 8
## 46 0.251 1.281 1.224 15
## 47 0.341 -6.571 -7.601 7
## 48 0.094 1.398 1.372 32
## 49 0.783 2.789 2.584 4
## 50 0.157 0.968 0.985 20
## 51 0.202 6.413 6.560 17
## 52 0.199 -3.576 -3.689 15
## 53 0.165 0.095 0.182 45
## 54 0.169 2.944 2.996 21
## 55 0.255 -0.489 -0.590 19
## 56 0.472 5.328 5.187 7
## 57 0.281 2.031 5.122 16
## 58 0.067 1.283 1.274 41
## 59 0.206 3.077 2.924 21
## 60 0.165 5.325 4.749 30
## 61 0.225 -7.264 -7.849 35
## 62 0.217 -3.912 -4.075 20
## 63 0.176 1.808 1.209 25
## 64 0.180 0.182 0.336 27
## 65 0.199 0.096 -0.242 19
## 66 0.199 0.273 0.000 26
## 67 0.172 2.155 3.476 33
## 68 0.210 1.676 1.691 15
## 69 0.790 5.687 5.050 3
## 70 0.352 -6.645 -6.502 11
## 71 0.206 -1.277 -1.341 19
## 72 0.187 -1.172 -1.144 11
## 73 0.251 1.281 1.131 13
## 74 0.307 -1.329 -1.466 17
## 75 0.479 -4.343 -4.423 8
## 76 0.820 6.213 6.293 4
## 77 0.146 1.530 1.853 32
## 78 0.161 -1.897 -2.120 29
## 79 0.184 5.359 3.690 21
## 80 0.176 2.944 2.833 22
## 81 0.180 2.332 3.266 36
## 82 0.300 -2.207 -2.120 18
## 83 0.210 -3.689 -3.963 14
## 84 0.333 -3.774 -3.345 24
## 85 0.434 -2.040 -1.609 11
## 86 0.416 4.466 4.554 8
## 87 0.472 4.489 3.912 6
## 88 0.622 5.288 5.187 6
## 89 0.262 0.094 0.177 25
## 90 0.127 -2.971 -2.902 26
## 91 0.322 3.738 3.714 10
## 92 0.127 2.833 2.996 38
## 93 0.303 0.182 0.336 14
## 94 0.213 0.095 0.182 32
## 95 0.161 -1.690 -1.710 26
## 96 0.090 -2.358 -2.259 36
## 97 0.202 -1.386 -1.772 16
## 98 0.146 -6.571 -6.502 28
## 99 0.221 -20.661 -23.322 14
## 100 0.131 0.946 1.055 22
## 101 0.281 -0.400 0.095 12
## 102 0.345 -2.207 -1.772 24
## 103 0.614 5.263 5.468 9
## 104 0.277 -6.032 -6.215 14
## 105 0.243 3.461 3.924 11
## 106 0.165 3.401 3.045 18
## 107 0.232 10.186 10.858 10
## 108 0.210 11.266 11.856 15
## 109 0.270 -0.755 -0.598 9
## 110 0.225 -0.683 -0.471 13
## 111 0.060 2.833 2.890 48
## 112 0.225 -3.096 -3.123 20
## 113 0.131 -1.579 -1.534 26
## 114 0.202 -0.886 -0.658 20
## 115 0.139 4.149 3.637 20
## 116 0.247 -6.190 -4.605 15
## 117 0.184 -1.715 -1.661 19
## 118 0.251 0.742 1.435 9
## 119 0.112 2.890 2.773 29
## 120 0.139 -4.017 -3.730 19
## 121 0.150 2.708 2.565 27
## 122 0.749 15.756 17.476 4
## 123 0.266 0.095 0.182 14
## 124 0.161 -4.269 -4.343 18
## Second.Mode.Count First.Second.Mode.Ratio Minimum Mean Median
## 1 11 1.273 -0.676 1.320 1.301
## 2 19 1.053 -2.207 -1.538 -1.561
## 3 26 1.115 -0.923 0.309 0.280
## 4 6 1.167 -6.725 -5.201 -5.185
## 5 10 1.200 0.262 1.361 1.361
## 6 10 1.100 -17.028 -13.052 -13.004
## 7 8 1.625 -4.343 -2.932 -2.937
## 8 20 1.050 -289.685 -158.615 -160.010
## 9 20 1.150 -0.545 0.673 0.642
## 10 7 1.286 1.752 2.318 2.320
## 11 21 1.429 -2.957 -1.854 -1.833
## 12 7 1.143 -8.680 -7.483 -7.470
## 13 8 1.125 -1.897 -0.635 -0.673
## 14 9 1.222 -9.937 -5.578 -5.703
## 15 18 1.278 -3.324 -1.583 -1.609
## 16 15 1.133 -3.689 -2.494 -2.526
## 17 10 1.100 0.470 1.440 1.411
## 18 10 1.100 0.591 2.806 2.818
## 19 9 1.556 -2.234 -0.321 -0.370
## 20 30 1.200 0.732 2.017 1.981
## 21 29 1.034 -2.761 -1.911 -1.877
## 22 36 1.056 -0.545 0.168 0.182
## 23 31 1.613 10.000 51.011 51.000
## 24 6 1.167 -8.517 -5.874 -5.843
## 25 20 1.250 -1.864 -1.258 -1.273
## 26 16 1.062 -1.238 -0.053 -0.062
## 27 6 1.333 10.961 22.433 22.249
## 28 13 1.154 -0.713 1.679 1.649
## 29 8 1.125 135.605 333.298 331.520
## 30 21 1.190 1.872 2.882 2.890
## 31 13 1.154 -23.387 -15.610 -15.524
## 32 19 1.158 -0.839 3.554 3.600
## 33 24 1.042 0.182 0.774 0.788
## 34 34 1.118 0.100 11.984 12.000
## 35 24 1.125 -1.872 -1.674 -1.671
## 36 3 1.333 7.432 8.586 8.564
## 37 11 1.091 -1.361 -0.701 -0.684
## 38 10 1.100 -8.377 -3.635 -3.650
## 39 22 1.773 -1.405 -1.372 -1.374
## 40 26 1.462 7.000 58.172 59.000
## 41 19 1.211 -1.514 -0.529 -0.528
## 42 9 1.111 -2.115 -1.143 -1.136
## 43 14 1.357 -0.154 2.968 3.101
## 44 8 1.125 -1.044 1.353 1.387
## 45 7 1.143 0.608 2.765 2.775
## 46 12 1.250 0.470 1.350 1.308
## 47 6 1.167 -8.874 -7.356 -7.323
## 48 28 1.143 1.271 1.378 1.382
## 49 3 1.333 2.393 2.786 2.783
## 50 18 1.111 0.524 0.951 0.968
## 51 13 1.308 2.103 6.833 6.703
## 52 14 1.071 -4.510 -3.500 -3.507
## 53 27 1.667 -0.635 0.196 0.182
## 54 19 1.105 1.758 2.958 2.944
## 55 14 1.357 -1.533 -0.591 -0.590
## 56 6 1.167 4.635 5.317 5.323
## 57 10 1.600 1.755 4.725 4.805
## 58 38 1.079 1.259 1.284 1.283
## 59 20 1.050 1.187 2.929 2.909
## 60 25 1.200 1.052 4.855 4.749
## 61 26 1.346 -8.517 -7.514 -7.524
## 62 17 1.176 -5.915 -3.941 -3.912
## 63 20 1.250 0.531 1.773 1.808
## 64 26 1.038 -1.427 0.187 0.182
## 65 16 1.188 -1.534 -0.154 -0.160
## 66 24 1.083 -0.676 0.095 0.097
## 67 18 1.833 0.560 2.839 2.793
## 68 13 1.154 1.574 1.704 1.705
## 69 2 1.500 4.317 5.755 5.753
## 70 10 1.100 -10.520 -6.121 -6.119
## 71 16 1.188 -2.169 -1.233 -1.246
## 72 10 1.100 -1.256 -1.185 -1.183
## 73 12 1.083 0.000 1.283 1.281
## 74 15 1.133 -2.147 -1.504 -1.505
## 75 7 1.143 -6.812 -4.417 -4.605
## 76 3 1.333 5.826 6.497 6.494
## 77 24 1.333 0.401 1.869 1.853
## 78 25 1.160 -2.847 -1.864 -1.897
## 79 14 1.500 0.935 4.049 4.050
## 80 21 1.048 1.946 2.814 2.833
## 81 22 1.636 0.098 2.875 2.815
## 82 17 1.059 -4.423 -2.446 -2.453
## 83 13 1.077 -4.934 -3.635 -3.650
## 84 13 1.846 -8.398 -3.789 -3.774
## 85 10 1.100 -3.170 -1.367 -1.470
## 86 7 1.143 3.178 4.552 4.554
## 87 5 1.200 2.639 4.362 4.394
## 88 5 1.200 4.111 5.204 5.187
## 89 20 1.250 -0.991 0.077 0.094
## 90 25 1.040 -3.311 -2.854 -2.871
## 91 9 1.111 2.485 3.912 3.871
## 92 27 1.407 2.186 3.015 2.996
## 93 11 1.273 -2.120 -0.013 -0.041
## 94 30 1.067 -1.309 0.045 0.000
## 95 21 1.238 -1.934 -1.685 -1.690
## 96 30 1.200 -3.338 -2.240 -2.259
## 97 14 1.143 -2.513 -1.488 -1.514
## 98 22 1.273 -7.222 -6.511 -6.502
## 99 13 1.077 -34.967 -17.641 -17.466
## 100 20 1.100 0.187 1.251 1.254
## 101 10 1.200 -1.347 -0.406 -0.400
## 102 11 2.182 -4.135 -2.477 -2.489
## 103 6 1.500 4.317 5.336 5.366
## 104 12 1.167 -7.506 -6.017 -6.032
## 105 10 1.100 1.654 3.852 3.867
## 106 15 1.200 2.251 3.301 3.296
## 107 9 1.111 6.843 9.801 9.919
## 108 13 1.154 1.742 11.750 11.565
## 109 8 1.125 -1.661 -0.594 -0.598
## 110 12 1.083 -1.211 -0.539 -0.532
## 111 47 1.021 2.485 2.854 2.833
## 112 18 1.111 -3.206 -3.116 -3.117
## 113 21 1.238 -2.038 -1.505 -1.492
## 114 17 1.176 -1.540 -0.754 -0.751
## 115 19 1.053 1.508 3.848 3.810
## 116 14 1.071 -6.190 -4.499 -4.510
## 117 18 1.056 -2.477 -1.479 -1.514
## 118 8 1.125 -0.211 1.170 1.224
## 119 28 1.036 1.932 2.909 2.890
## 120 18 1.056 -4.744 -3.876 -3.863
## 121 24 1.125 1.723 2.688 2.708
## 122 3 1.333 11.831 16.988 17.077
## 123 12 1.167 -1.427 -0.285 -0.301
## 124 17 1.059 -4.991 -3.906 -3.912
## Maximum Skewness Kurtosis Percentile25th Percentile75th
## 1 2.840 -0.101 2.975 0.946 1.719
## 2 -0.844 0.028 2.746 -1.715 -1.347
## 3 1.521 0.038 2.866 0.000 0.608
## 4 -3.507 0.108 2.713 -5.669 -4.780
## 5 2.303 0.034 3.103 1.131 1.589
## 6 -8.192 0.164 3.586 -14.071 -12.096
## 7 -1.772 0.027 2.648 -3.270 -2.590
## 8 -59.456 -0.034 3.026 -186.641 -134.622
## 9 1.526 -0.168 3.639 0.470 0.875
## 10 2.881 -0.042 2.370 2.119 2.497
## 11 -0.777 0.046 3.028 -2.120 -1.609
## 12 -6.166 -0.016 3.085 -7.763 -7.209
## 13 0.956 0.279 2.957 -0.968 -0.315
## 14 -2.153 -0.032 2.767 -6.630 -4.539
## 15 -0.274 0.088 4.317 -1.833 -1.367
## 16 -1.238 0.245 3.246 -2.773 -2.207
## 17 2.272 0.001 2.732 1.209 1.668
## 18 5.444 0.064 3.288 2.334 3.286
## 19 0.927 0.097 4.888 -0.598 -0.061
## 20 4.024 0.050 3.410 1.673 2.371
## 21 -0.817 0.055 3.667 -2.152 -1.675
## 22 0.993 -0.022 2.906 -0.041 0.336
## 23 82.000 -0.026 3.431 42.000 59.000
## 24 -2.937 0.032 2.515 -6.645 -5.083
## 25 -0.547 0.157 3.504 -1.376 -1.124
## 26 1.163 0.095 2.918 -0.357 0.262
## 27 33.777 -0.042 3.250 19.771 24.795
## 28 3.892 0.109 2.731 0.956 2.282
## 29 535.397 -0.055 2.708 278.025 392.067
## 30 3.584 -0.186 3.336 2.708 3.045
## 31 -9.563 -0.028 2.547 -17.567 -13.882
## 32 7.624 0.065 3.873 2.753 4.255
## 33 1.411 0.015 3.002 0.642 0.916
## 34 29.000 0.586 5.953 9.800 14.000
## 35 -1.384 0.086 3.197 -1.724 -1.626
## 36 9.694 0.157 3.075 8.321 8.839
## 37 -0.061 -0.163 2.797 -0.857 -0.546
## 38 -0.386 0.068 6.513 -4.200 -3.147
## 39 -1.339 0.091 2.965 -1.381 -1.364
## 40 107.000 0.074 2.842 44.000 70.000
## 41 0.336 -0.085 2.755 -0.713 -0.315
## 42 0.097 0.114 2.612 -1.466 -0.876
## 43 7.633 0.000 4.024 2.341 3.695
## 44 3.706 0.037 3.003 0.800 1.885
## 45 4.633 -0.127 2.981 2.290 3.292
## 46 2.251 0.165 2.609 1.099 1.609
## 47 -5.843 0.006 3.171 -7.717 -7.013
## 48 1.495 -0.219 2.941 1.351 1.406
## 49 3.065 -0.158 2.829 2.707 2.873
## 50 1.318 0.044 2.589 0.844 1.034
## 51 10.695 0.020 2.900 5.786 7.865
## 52 -2.120 0.260 3.591 -3.730 -3.270
## 53 0.875 -0.055 2.708 0.000 0.405
## 54 4.143 -0.174 3.467 2.708 3.219
## 55 0.517 -0.040 2.979 -0.830 -0.383
## 56 5.948 -0.110 3.296 5.179 5.453
## 57 8.491 0.000 2.480 3.706 5.776
## 58 1.321 0.402 3.316 1.274 1.290
## 59 4.937 0.175 3.117 2.521 3.351
## 60 8.952 -0.012 3.326 4.149 5.631
## 61 -5.952 0.299 3.530 -7.824 -7.264
## 62 -2.453 -0.150 3.642 -4.269 -3.631
## 63 3.045 0.004 2.822 1.459 2.146
## 64 1.946 -0.045 3.687 -0.122 0.470
## 65 1.814 0.016 4.021 -0.413 0.141
## 66 0.831 -0.132 2.443 -0.125 0.354
## 67 5.706 -0.022 2.488 2.155 3.706
## 68 1.807 -0.145 3.470 1.680 1.728
## 69 7.501 0.293 3.317 5.398 6.064
## 70 -4.200 -0.607 6.399 -6.645 -5.573
## 71 -0.159 -0.025 3.691 -1.447 -1.034
## 72 -1.105 0.012 2.880 -1.204 -1.164
## 73 2.272 -0.020 3.061 1.030 1.526
## 74 -0.621 0.053 3.019 -1.700 -1.329
## 75 -1.386 0.344 2.390 -5.308 -3.490
## 76 7.230 -0.015 2.788 6.319 6.678
## 77 4.024 0.000 3.957 1.530 2.182
## 78 -0.844 0.160 3.034 -2.120 -1.661
## 79 6.796 0.151 2.810 3.338 4.686
## 80 4.007 0.206 3.001 2.565 3.045
## 81 5.359 -0.113 2.953 2.332 3.551
## 82 -0.528 -0.114 3.615 -2.749 -2.120
## 83 -2.207 0.240 3.361 -3.938 -3.352
## 84 -0.222 -0.035 2.703 -4.820 -2.714
## 85 1.775 0.704 3.638 -2.040 -0.799
## 86 5.886 -0.160 4.130 4.350 4.775
## 87 6.011 -0.244 3.244 3.998 4.749
## 88 6.308 0.031 3.223 4.963 5.442
## 89 1.166 0.045 2.750 -0.167 0.320
## 90 -2.520 -0.029 2.698 -2.936 -2.749
## 91 5.170 -0.092 3.011 3.638 4.205
## 92 3.932 0.166 3.434 2.833 3.178
## 93 1.932 0.076 2.690 -0.528 0.531
## 94 0.993 -0.045 4.693 -0.139 0.258
## 95 -1.424 0.193 5.276 -1.717 -1.654
## 96 -1.221 0.041 3.459 -2.464 -2.000
## 97 -0.274 0.244 2.621 -1.833 -1.171
## 98 -5.547 0.170 2.912 -6.725 -6.320
## 99 -2.239 -0.050 2.997 -21.468 -13.501
## 100 2.373 -0.007 2.971 1.001 1.500
## 101 0.742 0.142 3.370 -0.635 -0.198
## 102 -1.109 -0.066 2.965 -2.813 -2.120
## 103 6.317 -0.145 3.061 5.094 5.583
## 104 -4.646 -0.127 2.900 -6.377 -5.655
## 105 6.225 0.056 2.885 3.343 4.371
## 106 4.277 0.056 2.906 3.045 3.526
## 107 13.827 -0.021 2.809 8.859 10.695
## 108 18.881 0.157 6.004 10.490 12.697
## 109 0.470 0.044 3.110 -0.821 -0.378
## 110 0.269 0.070 3.256 -0.701 -0.385
## 111 3.332 0.226 3.255 2.773 2.944
## 112 -2.995 0.059 3.762 -3.137 -3.096
## 113 -0.817 -0.024 2.875 -1.626 -1.341
## 114 0.098 0.239 3.382 -0.886 -0.629
## 115 6.225 0.032 3.693 3.343 4.316
## 116 -1.715 0.024 3.719 -4.962 -4.017
## 117 -0.211 0.331 2.879 -1.772 -1.238
## 118 2.485 -0.180 2.887 0.833 1.482
## 119 3.761 -0.035 3.828 2.708 3.091
## 120 -2.957 0.151 2.781 -4.135 -3.650
## 121 3.689 -0.064 3.124 2.485 2.890
## 122 22.380 0.102 3.290 15.773 18.095
## 123 0.531 -0.038 2.760 -0.511 -0.036
## 124 -2.957 -0.124 2.625 -4.200 -3.612##################################
# Identifying potential data quality issues
##################################
##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
print(paste0("Missing observations noted for ",
(nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
" variable(s) with NA.Count>0 and Fill.Rate<1.0."))
DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
print("No missing observations noted.")
}## [1] "No missing observations noted."##################################
# Checking for zero or near-zero variance predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
print("No factor predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
" factor variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance factor predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 2 factor variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 2 E3 character 2 1 0
## 3 E2 character 2 0 1
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 2 245 22 0.007 11.136
## 3 224 43 0.007 5.209if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
" numeric variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric predictors due to high first-second mode ratio noted.")
}## [1] "No low variance numeric predictors due to high first-second mode ratio noted."if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
" numeric variable(s) with Unique.Count.Ratio<0.01."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
print("No low variance numeric predictors due to low unique count ratio noted.")
}## [1] "No low variance numeric predictors due to low unique count ratio noted."##################################
# Checking for skewed predictors
##################################
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])>0){
print(paste0("High skewness observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])),
" numeric variable(s) with Skewness>3 or Skewness<(-3)."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),]
} else {
print("No skewed numeric predictors noted.")
}## [1] "No skewed numeric predictors noted."1.3 Data Preprocessing
1.3.1 Outlier
Outlier data assessment:
[A] Few outliers noted for most variables with the numeric data visualized through a boxplot including observations classified as suspected outliers using the IQR criterion. The IQR criterion means that all observations above the (75th percentile + 1.5 x IQR) or below the (25th percentile - 1.5 x IQR) are suspected outliers, where IQR is the difference between the third quartile (75th percentile) and first quartile (25th percentile). Outlier treatment for numerical stability remains optional depending on potential model requirements for the subsequent steps. 6 variables were observed with at least 10 outliers listed as follows:
[A.1] Apolipoprotein_CI variable (10 outliers detected)
[A.2] Cortisol variable (11 outliers detected)
[A.3] IL_17E variable (11 outliers detected)
[A.4] IL6 variable (19 outliers detected)
[A.5] MCP_2 variable (21 outliers detected)
[A.6] Prostatic_Acid_Phospatase variable (10 outliers detected)
##################################
# Loading dataset
##################################
DPA <- Alzheimer_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,!names(DPA.Predictors) %in% c("E2","E3","E4")]
DPA.Predictors.Numeric <- as.data.frame(sapply(DPA.Predictors.Numeric,function(x) as.numeric(x)))
##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
boxplot(DPA.Predictors.Numeric[,i],
ylab = names(DPA.Predictors.Numeric)[i],
main = names(DPA.Predictors.Numeric)[i],
horizontal=TRUE)
mtext(paste0(OutlierCount, " Outlier(s) Detected"))
}
OutlierCountSummary <- as.data.frame(cbind(names(DPA.Predictors.Numeric),(OutlierCountList)))
names(OutlierCountSummary) <- c("NumericPredictors","OutlierCount")
OutlierCountSummary$OutlierCount <- as.numeric(as.character(OutlierCountSummary$OutlierCount))
NumericPredictorWithOutlierCount <- nrow(OutlierCountSummary[OutlierCountSummary$OutlierCount>0,])
print(paste0(NumericPredictorWithOutlierCount, " numeric variable(s) were noted with outlier(s)." ))## [1] "105 numeric variable(s) were noted with outlier(s)."##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA.Predictors.Numeric))| Name | DPA.Predictors.Numeric |
| Number of rows | 267 |
| Number of columns | 124 |
| _______________________ | |
| Column type frequency: | |
| numeric | 124 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ACE_CD143_Angiotensin_Converti | 0 | 1 | 1.32 | 0.54 | -0.68 | 0.95 | 1.30 | 1.72 | 2.84 | ▁▃▇▇▁ |
| ACTH_Adrenocorticotropic_Hormon | 0 | 1 | -1.54 | 0.28 | -2.21 | -1.71 | -1.56 | -1.35 | -0.84 | ▂▆▇▆▂ |
| AXL | 0 | 1 | 0.31 | 0.45 | -0.92 | 0.00 | 0.28 | 0.61 | 1.52 | ▁▅▇▃▁ |
| Adiponectin | 0 | 1 | -5.20 | 0.67 | -6.73 | -5.67 | -5.18 | -4.78 | -3.51 | ▂▆▇▅▁ |
| Alpha_1_Antichymotrypsin | 0 | 1 | 1.36 | 0.36 | 0.26 | 1.13 | 1.36 | 1.59 | 2.30 | ▁▃▇▅▁ |
| Alpha_1_Antitrypsin | 0 | 1 | -13.05 | 1.48 | -17.03 | -14.07 | -13.00 | -12.10 | -8.19 | ▁▅▇▂▁ |
| Alpha_1_Microglobulin | 0 | 1 | -2.93 | 0.48 | -4.34 | -3.27 | -2.94 | -2.59 | -1.77 | ▁▅▇▇▂ |
| Alpha_2_Macroglobulin | 0 | 1 | -158.61 | 40.93 | -289.68 | -186.64 | -160.01 | -134.62 | -59.46 | ▁▂▇▅▂ |
| Angiopoietin_2_ANG_2 | 0 | 1 | 0.67 | 0.33 | -0.54 | 0.47 | 0.64 | 0.88 | 1.53 | ▁▂▇▆▂ |
| Angiotensinogen | 0 | 1 | 2.32 | 0.25 | 1.75 | 2.12 | 2.32 | 2.50 | 2.88 | ▃▆▇▇▂ |
| Apolipoprotein_A_IV | 0 | 1 | -1.85 | 0.38 | -2.96 | -2.12 | -1.83 | -1.61 | -0.78 | ▁▃▇▃▁ |
| Apolipoprotein_A1 | 0 | 1 | -7.48 | 0.44 | -8.68 | -7.76 | -7.47 | -7.21 | -6.17 | ▁▅▇▃▁ |
| Apolipoprotein_A2 | 0 | 1 | -0.64 | 0.50 | -1.90 | -0.97 | -0.67 | -0.31 | 0.96 | ▁▇▇▃▁ |
| Apolipoprotein_B | 0 | 1 | -5.58 | 1.48 | -9.94 | -6.63 | -5.70 | -4.54 | -2.15 | ▁▅▇▇▂ |
| Apolipoprotein_CI | 0 | 1 | -1.58 | 0.41 | -3.32 | -1.83 | -1.61 | -1.37 | -0.27 | ▁▁▇▅▁ |
| Apolipoprotein_CIII | 0 | 1 | -2.49 | 0.44 | -3.69 | -2.77 | -2.53 | -2.21 | -1.24 | ▁▅▇▅▁ |
| Apolipoprotein_D | 0 | 1 | 1.44 | 0.34 | 0.47 | 1.21 | 1.41 | 1.67 | 2.27 | ▁▃▇▆▂ |
| Apolipoprotein_E | 0 | 1 | 2.81 | 0.78 | 0.59 | 2.33 | 2.82 | 3.29 | 5.44 | ▁▅▇▂▁ |
| Apolipoprotein_H | 0 | 1 | -0.32 | 0.39 | -2.23 | -0.60 | -0.37 | -0.06 | 0.93 | ▁▁▇▆▁ |
| B_Lymphocyte_Chemoattractant_BL | 0 | 1 | 2.02 | 0.57 | 0.73 | 1.67 | 1.98 | 2.37 | 4.02 | ▃▇▇▁▁ |
| BMP_6 | 0 | 1 | -1.91 | 0.31 | -2.76 | -2.15 | -1.88 | -1.68 | -0.82 | ▁▅▇▂▁ |
| Beta_2_Microglobulin | 0 | 1 | 0.17 | 0.30 | -0.54 | -0.04 | 0.18 | 0.34 | 0.99 | ▂▃▇▃▁ |
| Betacellulin | 0 | 1 | 51.01 | 10.87 | 10.00 | 42.00 | 51.00 | 59.00 | 82.00 | ▁▁▇▅▁ |
| C_Reactive_Protein | 0 | 1 | -5.87 | 1.20 | -8.52 | -6.65 | -5.84 | -5.08 | -2.94 | ▃▆▇▆▂ |
| CD40 | 0 | 1 | -1.26 | 0.21 | -1.86 | -1.38 | -1.27 | -1.12 | -0.55 | ▁▆▇▃▁ |
| CD5L | 0 | 1 | -0.05 | 0.45 | -1.24 | -0.36 | -0.06 | 0.26 | 1.16 | ▁▅▇▃▁ |
| Calbindin | 0 | 1 | 22.43 | 4.11 | 10.96 | 19.77 | 22.25 | 24.80 | 33.78 | ▁▃▇▃▁ |
| Calcitonin | 0 | 1 | 1.68 | 0.87 | -0.71 | 0.96 | 1.65 | 2.28 | 3.89 | ▁▆▇▅▂ |
| CgA | 0 | 1 | 333.30 | 83.67 | 135.60 | 278.02 | 331.52 | 392.07 | 535.40 | ▂▆▇▅▂ |
| Clusterin_Apo_J | 0 | 1 | 2.88 | 0.29 | 1.87 | 2.71 | 2.89 | 3.04 | 3.58 | ▁▂▇▆▂ |
| Complement_3 | 0 | 1 | -15.61 | 2.46 | -23.39 | -17.57 | -15.52 | -13.88 | -9.56 | ▁▆▇▇▂ |
| Complement_Factor_H | 0 | 1 | 3.55 | 1.25 | -0.84 | 2.75 | 3.60 | 4.25 | 7.62 | ▁▃▇▅▁ |
| Connective_Tissue_Growth_Factor | 0 | 1 | 0.77 | 0.20 | 0.18 | 0.64 | 0.79 | 0.92 | 1.41 | ▁▅▇▃▁ |
| Cortisol | 0 | 1 | 11.98 | 3.95 | 0.10 | 9.80 | 12.00 | 14.00 | 29.00 | ▁▇▇▁▁ |
| Creatine_Kinase_MB | 0 | 1 | -1.67 | 0.09 | -1.87 | -1.72 | -1.67 | -1.63 | -1.38 | ▃▅▇▂▁ |
| Cystatin_C | 0 | 1 | 8.59 | 0.40 | 7.43 | 8.32 | 8.56 | 8.84 | 9.69 | ▁▅▇▃▁ |
| EGF_R | 0 | 1 | -0.70 | 0.22 | -1.36 | -0.86 | -0.68 | -0.55 | -0.06 | ▁▃▇▅▁ |
| EN_RAGE | 0 | 1 | -3.64 | 0.88 | -8.38 | -4.20 | -3.65 | -3.15 | -0.39 | ▁▁▇▆▁ |
| ENA_78 | 0 | 1 | -1.37 | 0.01 | -1.41 | -1.38 | -1.37 | -1.36 | -1.34 | ▁▅▇▅▁ |
| Eotaxin_3 | 0 | 1 | 58.17 | 15.83 | 7.00 | 44.00 | 59.00 | 70.00 | 107.00 | ▁▆▇▅▁ |
| FAS | 0 | 1 | -0.53 | 0.30 | -1.51 | -0.71 | -0.53 | -0.31 | 0.34 | ▁▃▇▆▁ |
| FSH_Follicle_Stimulation_Hormon | 0 | 1 | -1.14 | 0.42 | -2.12 | -1.47 | -1.14 | -0.88 | 0.10 | ▂▇▇▃▁ |
| Fas_Ligand | 0 | 1 | 2.97 | 1.09 | -0.15 | 2.34 | 3.10 | 3.70 | 7.63 | ▂▇▇▁▁ |
| Fatty_Acid_Binding_Protein | 0 | 1 | 1.35 | 0.78 | -1.04 | 0.80 | 1.39 | 1.88 | 3.71 | ▁▅▇▅▁ |
| Ferritin | 0 | 1 | 2.76 | 0.76 | 0.61 | 2.29 | 2.77 | 3.29 | 4.63 | ▁▃▇▆▂ |
| Fetuin_A | 0 | 1 | 1.35 | 0.37 | 0.47 | 1.10 | 1.31 | 1.61 | 2.25 | ▂▆▇▅▂ |
| Fibrinogen | 0 | 1 | -7.36 | 0.57 | -8.87 | -7.72 | -7.32 | -7.01 | -5.84 | ▁▅▇▅▁ |
| GRO_alpha | 0 | 1 | 1.38 | 0.04 | 1.27 | 1.35 | 1.38 | 1.41 | 1.49 | ▂▃▇▅▁ |
| Gamma_Interferon_induced_Monokin | 0 | 1 | 2.79 | 0.11 | 2.39 | 2.71 | 2.78 | 2.87 | 3.07 | ▁▂▇▆▂ |
| Glutathione_S_Transferase_alpha | 0 | 1 | 0.95 | 0.16 | 0.52 | 0.84 | 0.97 | 1.03 | 1.32 | ▁▃▇▅▂ |
| HB_EGF | 0 | 1 | 6.83 | 1.51 | 2.10 | 5.79 | 6.70 | 7.86 | 10.70 | ▁▃▇▆▂ |
| HCC_4 | 0 | 1 | -3.50 | 0.36 | -4.51 | -3.73 | -3.51 | -3.27 | -2.12 | ▂▇▇▂▁ |
| Hepatocyte_Growth_Factor_HGF | 0 | 1 | 0.20 | 0.29 | -0.63 | 0.00 | 0.18 | 0.41 | 0.88 | ▁▃▇▅▂ |
| I_309 | 0 | 1 | 2.96 | 0.37 | 1.76 | 2.71 | 2.94 | 3.22 | 4.14 | ▁▅▇▃▁ |
| ICAM_1 | 0 | 1 | -0.59 | 0.33 | -1.53 | -0.83 | -0.59 | -0.38 | 0.52 | ▁▅▇▃▁ |
| IGF_BP_2 | 0 | 1 | 5.32 | 0.20 | 4.63 | 5.18 | 5.32 | 5.45 | 5.95 | ▁▃▇▅▁ |
| IL_11 | 0 | 1 | 4.72 | 1.45 | 1.75 | 3.71 | 4.81 | 5.78 | 8.49 | ▃▆▇▅▁ |
| IL_13 | 0 | 1 | 1.28 | 0.01 | 1.26 | 1.27 | 1.28 | 1.29 | 1.32 | ▂▇▆▂▁ |
| IL_16 | 0 | 1 | 2.93 | 0.66 | 1.19 | 2.52 | 2.91 | 3.35 | 4.94 | ▁▆▇▃▁ |
| IL_17E | 0 | 1 | 4.85 | 1.34 | 1.05 | 4.15 | 4.75 | 5.63 | 8.95 | ▁▆▇▃▁ |
| IL_1alpha | 0 | 1 | -7.51 | 0.39 | -8.52 | -7.82 | -7.52 | -7.26 | -5.95 | ▂▇▇▂▁ |
| IL_3 | 0 | 1 | -3.94 | 0.50 | -5.91 | -4.27 | -3.91 | -3.63 | -2.45 | ▁▂▇▅▁ |
| IL_4 | 0 | 1 | 1.77 | 0.51 | 0.53 | 1.46 | 1.81 | 2.15 | 3.04 | ▂▅▇▅▂ |
| IL_5 | 0 | 1 | 0.19 | 0.46 | -1.43 | -0.12 | 0.18 | 0.47 | 1.95 | ▁▃▇▂▁ |
| IL_6 | 0 | 1 | -0.15 | 0.55 | -1.53 | -0.41 | -0.16 | 0.14 | 1.81 | ▂▆▇▂▁ |
| IL_6_Receptor | 0 | 1 | 0.09 | 0.32 | -0.68 | -0.13 | 0.10 | 0.35 | 0.83 | ▂▆▇▆▂ |
| IL_7 | 0 | 1 | 2.84 | 1.05 | 0.56 | 2.15 | 2.79 | 3.71 | 5.71 | ▃▇▇▆▁ |
| IL_8 | 0 | 1 | 1.70 | 0.04 | 1.57 | 1.68 | 1.71 | 1.73 | 1.81 | ▁▂▇▆▁ |
| IP_10_Inducible_Protein_10 | 0 | 1 | 5.75 | 0.51 | 4.32 | 5.40 | 5.75 | 6.06 | 7.50 | ▁▆▇▂▁ |
| IgA | 0 | 1 | -6.12 | 0.76 | -10.52 | -6.65 | -6.12 | -5.57 | -4.20 | ▁▁▂▇▂ |
| Insulin | 0 | 1 | -1.23 | 0.34 | -2.17 | -1.45 | -1.25 | -1.03 | -0.16 | ▁▃▇▂▁ |
| Kidney_Injury_Molecule_1_KIM_1 | 0 | 1 | -1.18 | 0.03 | -1.26 | -1.20 | -1.18 | -1.16 | -1.10 | ▁▇▇▅▁ |
| LOX_1 | 0 | 1 | 1.28 | 0.40 | 0.00 | 1.03 | 1.28 | 1.53 | 2.27 | ▁▂▇▅▂ |
| Leptin | 0 | 1 | -1.50 | 0.27 | -2.15 | -1.70 | -1.50 | -1.33 | -0.62 | ▂▇▇▂▁ |
| Lipoprotein_a | 0 | 1 | -4.42 | 1.11 | -6.81 | -5.31 | -4.61 | -3.49 | -1.39 | ▂▇▅▅▁ |
| MCP_1 | 0 | 1 | 6.50 | 0.26 | 5.83 | 6.32 | 6.49 | 6.68 | 7.23 | ▂▅▇▅▁ |
| MCP_2 | 0 | 1 | 1.87 | 0.65 | 0.40 | 1.53 | 1.85 | 2.18 | 4.02 | ▂▆▇▂▁ |
| MIF | 0 | 1 | -1.86 | 0.34 | -2.85 | -2.12 | -1.90 | -1.66 | -0.84 | ▁▃▇▃▁ |
| MIP_1alpha | 0 | 1 | 4.05 | 1.01 | 0.93 | 3.34 | 4.05 | 4.69 | 6.80 | ▁▅▇▅▂ |
| MIP_1beta | 0 | 1 | 2.81 | 0.38 | 1.95 | 2.56 | 2.83 | 3.04 | 4.01 | ▂▅▇▂▁ |
| MMP_2 | 0 | 1 | 2.88 | 0.93 | 0.10 | 2.33 | 2.82 | 3.55 | 5.36 | ▁▃▇▆▁ |
| MMP_3 | 0 | 1 | -2.45 | 0.57 | -4.42 | -2.75 | -2.45 | -2.12 | -0.53 | ▁▂▇▃▁ |
| MMP10 | 0 | 1 | -3.63 | 0.43 | -4.93 | -3.94 | -3.65 | -3.35 | -2.21 | ▁▆▇▃▁ |
| MMP7 | 0 | 1 | -3.79 | 1.55 | -8.40 | -4.82 | -3.77 | -2.71 | -0.22 | ▁▃▇▅▂ |
| Myoglobin | 0 | 1 | -1.37 | 0.95 | -3.17 | -2.04 | -1.47 | -0.80 | 1.77 | ▅▇▆▂▁ |
| NT_proBNP | 0 | 1 | 4.55 | 0.38 | 3.18 | 4.35 | 4.55 | 4.77 | 5.89 | ▁▂▇▃▁ |
| NrCAM | 0 | 1 | 4.36 | 0.57 | 2.64 | 4.00 | 4.39 | 4.75 | 6.01 | ▁▃▇▅▁ |
| Osteopontin | 0 | 1 | 5.20 | 0.39 | 4.11 | 4.96 | 5.19 | 5.44 | 6.31 | ▁▅▇▃▁ |
| PAI_1 | 0 | 1 | 0.08 | 0.41 | -0.99 | -0.17 | 0.09 | 0.32 | 1.17 | ▂▃▇▅▂ |
| PAPP_A | 0 | 1 | -2.85 | 0.14 | -3.31 | -2.94 | -2.87 | -2.75 | -2.52 | ▁▂▇▅▂ |
| PLGF | 0 | 1 | 3.91 | 0.41 | 2.48 | 3.64 | 3.87 | 4.20 | 5.17 | ▁▃▇▆▁ |
| PYY | 0 | 1 | 3.02 | 0.29 | 2.19 | 2.83 | 3.00 | 3.18 | 3.93 | ▁▅▇▃▁ |
| Pancreatic_polypeptide | 0 | 1 | -0.01 | 0.72 | -2.12 | -0.53 | -0.04 | 0.53 | 1.93 | ▁▅▇▆▁ |
| Prolactin | 0 | 1 | 0.04 | 0.30 | -1.31 | -0.14 | 0.00 | 0.26 | 0.99 | ▁▁▇▇▁ |
| Prostatic_Acid_Phosphatase | 0 | 1 | -1.69 | 0.06 | -1.93 | -1.72 | -1.69 | -1.65 | -1.42 | ▁▂▇▂▁ |
| Protein_S | 0 | 1 | -2.24 | 0.35 | -3.34 | -2.46 | -2.26 | -2.00 | -1.22 | ▁▃▇▃▁ |
| Pulmonary_and_Activation_Regulat | 0 | 1 | -1.49 | 0.45 | -2.51 | -1.83 | -1.51 | -1.17 | -0.27 | ▂▇▇▃▂ |
| RANTES | 0 | 1 | -6.51 | 0.32 | -7.22 | -6.73 | -6.50 | -6.32 | -5.55 | ▃▇▇▃▁ |
| Resistin | 0 | 1 | -17.64 | 5.82 | -34.97 | -21.47 | -17.47 | -13.50 | -2.24 | ▁▃▇▆▁ |
| S100b | 0 | 1 | 1.25 | 0.34 | 0.19 | 1.00 | 1.25 | 1.50 | 2.37 | ▁▆▇▅▁ |
| SGOT | 0 | 1 | -0.41 | 0.35 | -1.35 | -0.63 | -0.40 | -0.20 | 0.74 | ▁▅▇▂▁ |
| SHBG | 0 | 1 | -2.48 | 0.58 | -4.14 | -2.81 | -2.49 | -2.12 | -1.11 | ▁▃▇▆▂ |
| SOD | 0 | 1 | 5.34 | 0.38 | 4.32 | 5.09 | 5.37 | 5.58 | 6.32 | ▁▅▇▅▁ |
| Serum_Amyloid_P | 0 | 1 | -6.02 | 0.56 | -7.51 | -6.38 | -6.03 | -5.65 | -4.65 | ▁▅▇▅▂ |
| Sortilin | 0 | 1 | 3.85 | 0.87 | 1.65 | 3.34 | 3.87 | 4.37 | 6.23 | ▁▅▇▃▁ |
| Stem_Cell_Factor | 0 | 1 | 3.30 | 0.36 | 2.25 | 3.04 | 3.30 | 3.53 | 4.28 | ▁▅▇▅▁ |
| TGF_alpha | 0 | 1 | 9.80 | 1.32 | 6.84 | 8.86 | 9.92 | 10.70 | 13.83 | ▂▆▇▂▁ |
| TIMP_1 | 0 | 1 | 11.75 | 1.90 | 1.74 | 10.49 | 11.56 | 12.70 | 18.88 | ▁▁▇▅▁ |
| TNF_RII | 0 | 1 | -0.59 | 0.33 | -1.66 | -0.82 | -0.60 | -0.38 | 0.47 | ▁▃▇▃▁ |
| TRAIL_R3 | 0 | 1 | -0.54 | 0.24 | -1.21 | -0.70 | -0.53 | -0.38 | 0.27 | ▁▆▇▂▁ |
| TTR_prealbumin | 0 | 1 | 2.85 | 0.14 | 2.48 | 2.77 | 2.83 | 2.94 | 3.33 | ▂▅▇▃▁ |
| Tamm_Horsfall_Protein_THP | 0 | 1 | -3.12 | 0.03 | -3.21 | -3.14 | -3.12 | -3.10 | -2.99 | ▁▇▇▂▁ |
| Thrombomodulin | 0 | 1 | -1.51 | 0.22 | -2.04 | -1.63 | -1.49 | -1.34 | -0.82 | ▂▆▇▃▁ |
| Thrombopoietin | 0 | 1 | -0.75 | 0.24 | -1.54 | -0.89 | -0.75 | -0.63 | 0.10 | ▁▅▇▂▁ |
| Thymus_Expressed_Chemokine_TECK | 0 | 1 | 3.85 | 0.80 | 1.51 | 3.34 | 3.81 | 4.32 | 6.23 | ▁▃▇▃▁ |
| Thyroid_Stimulating_Hormone | 0 | 1 | -4.50 | 0.75 | -6.19 | -4.96 | -4.51 | -4.02 | -1.71 | ▂▇▆▁▁ |
| Thyroxine_Binding_Globulin | 0 | 1 | -1.48 | 0.40 | -2.48 | -1.77 | -1.51 | -1.24 | -0.21 | ▂▇▇▃▁ |
| Tissue_Factor | 0 | 1 | 1.17 | 0.50 | -0.21 | 0.83 | 1.22 | 1.48 | 2.48 | ▁▅▇▆▁ |
| Transferrin | 0 | 1 | 2.91 | 0.27 | 1.93 | 2.71 | 2.89 | 3.09 | 3.76 | ▁▂▇▅▁ |
| Trefoil_Factor_3_TFF3 | 0 | 1 | -3.88 | 0.34 | -4.74 | -4.14 | -3.86 | -3.65 | -2.96 | ▁▅▇▃▁ |
| VCAM_1 | 0 | 1 | 2.69 | 0.32 | 1.72 | 2.48 | 2.71 | 2.89 | 3.69 | ▁▃▇▃▁ |
| VEGF | 0 | 1 | 16.99 | 1.81 | 11.83 | 15.77 | 17.08 | 18.10 | 22.38 | ▁▅▇▃▁ |
| Vitronectin | 0 | 1 | -0.28 | 0.33 | -1.43 | -0.51 | -0.30 | -0.04 | 0.53 | ▁▃▇▇▂ |
| von_Willebrand_Factor | 0 | 1 | -3.91 | 0.38 | -4.99 | -4.20 | -3.91 | -3.61 | -2.96 | ▁▅▇▆▂ |
###################################
# Verifying the data dimensions
###################################
dim(DPA.Predictors.Numeric)## [1] 267 1241.3.2 Zero and Near-Zero Variance
Zero and near-zero variance data assessment:
[A] Low variance noted for 2 variables from the previous data quality assessment using a lower threshold.
[B] No low variance noted for any variable using a preprocessing summary from the caret package. The nearZeroVar method using both the freqCut and uniqueCut criteria set at 95/5 and 10, respectively, were applied on the dataset.
##################################
# Loading dataset
##################################
DPA <- DPA.Predictors
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA))| Name | DPA |
| Number of rows | 267 |
| Number of columns | 127 |
| _______________________ | |
| Column type frequency: | |
| numeric | 127 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ACE_CD143_Angiotensin_Converti | 0 | 1 | 1.32 | 0.54 | -0.68 | 0.95 | 1.30 | 1.72 | 2.84 | ▁▃▇▇▁ |
| ACTH_Adrenocorticotropic_Hormon | 0 | 1 | -1.54 | 0.28 | -2.21 | -1.71 | -1.56 | -1.35 | -0.84 | ▂▆▇▆▂ |
| AXL | 0 | 1 | 0.31 | 0.45 | -0.92 | 0.00 | 0.28 | 0.61 | 1.52 | ▁▅▇▃▁ |
| Adiponectin | 0 | 1 | -5.20 | 0.67 | -6.73 | -5.67 | -5.18 | -4.78 | -3.51 | ▂▆▇▅▁ |
| Alpha_1_Antichymotrypsin | 0 | 1 | 1.36 | 0.36 | 0.26 | 1.13 | 1.36 | 1.59 | 2.30 | ▁▃▇▅▁ |
| Alpha_1_Antitrypsin | 0 | 1 | -13.05 | 1.48 | -17.03 | -14.07 | -13.00 | -12.10 | -8.19 | ▁▅▇▂▁ |
| Alpha_1_Microglobulin | 0 | 1 | -2.93 | 0.48 | -4.34 | -3.27 | -2.94 | -2.59 | -1.77 | ▁▅▇▇▂ |
| Alpha_2_Macroglobulin | 0 | 1 | -158.61 | 40.93 | -289.68 | -186.64 | -160.01 | -134.62 | -59.46 | ▁▂▇▅▂ |
| Angiopoietin_2_ANG_2 | 0 | 1 | 0.67 | 0.33 | -0.54 | 0.47 | 0.64 | 0.88 | 1.53 | ▁▂▇▆▂ |
| Angiotensinogen | 0 | 1 | 2.32 | 0.25 | 1.75 | 2.12 | 2.32 | 2.50 | 2.88 | ▃▆▇▇▂ |
| Apolipoprotein_A_IV | 0 | 1 | -1.85 | 0.38 | -2.96 | -2.12 | -1.83 | -1.61 | -0.78 | ▁▃▇▃▁ |
| Apolipoprotein_A1 | 0 | 1 | -7.48 | 0.44 | -8.68 | -7.76 | -7.47 | -7.21 | -6.17 | ▁▅▇▃▁ |
| Apolipoprotein_A2 | 0 | 1 | -0.64 | 0.50 | -1.90 | -0.97 | -0.67 | -0.31 | 0.96 | ▁▇▇▃▁ |
| Apolipoprotein_B | 0 | 1 | -5.58 | 1.48 | -9.94 | -6.63 | -5.70 | -4.54 | -2.15 | ▁▅▇▇▂ |
| Apolipoprotein_CI | 0 | 1 | -1.58 | 0.41 | -3.32 | -1.83 | -1.61 | -1.37 | -0.27 | ▁▁▇▅▁ |
| Apolipoprotein_CIII | 0 | 1 | -2.49 | 0.44 | -3.69 | -2.77 | -2.53 | -2.21 | -1.24 | ▁▅▇▅▁ |
| Apolipoprotein_D | 0 | 1 | 1.44 | 0.34 | 0.47 | 1.21 | 1.41 | 1.67 | 2.27 | ▁▃▇▆▂ |
| Apolipoprotein_E | 0 | 1 | 2.81 | 0.78 | 0.59 | 2.33 | 2.82 | 3.29 | 5.44 | ▁▅▇▂▁ |
| Apolipoprotein_H | 0 | 1 | -0.32 | 0.39 | -2.23 | -0.60 | -0.37 | -0.06 | 0.93 | ▁▁▇▆▁ |
| B_Lymphocyte_Chemoattractant_BL | 0 | 1 | 2.02 | 0.57 | 0.73 | 1.67 | 1.98 | 2.37 | 4.02 | ▃▇▇▁▁ |
| BMP_6 | 0 | 1 | -1.91 | 0.31 | -2.76 | -2.15 | -1.88 | -1.68 | -0.82 | ▁▅▇▂▁ |
| Beta_2_Microglobulin | 0 | 1 | 0.17 | 0.30 | -0.54 | -0.04 | 0.18 | 0.34 | 0.99 | ▂▃▇▃▁ |
| Betacellulin | 0 | 1 | 51.01 | 10.87 | 10.00 | 42.00 | 51.00 | 59.00 | 82.00 | ▁▁▇▅▁ |
| C_Reactive_Protein | 0 | 1 | -5.87 | 1.20 | -8.52 | -6.65 | -5.84 | -5.08 | -2.94 | ▃▆▇▆▂ |
| CD40 | 0 | 1 | -1.26 | 0.21 | -1.86 | -1.38 | -1.27 | -1.12 | -0.55 | ▁▆▇▃▁ |
| CD5L | 0 | 1 | -0.05 | 0.45 | -1.24 | -0.36 | -0.06 | 0.26 | 1.16 | ▁▅▇▃▁ |
| Calbindin | 0 | 1 | 22.43 | 4.11 | 10.96 | 19.77 | 22.25 | 24.80 | 33.78 | ▁▃▇▃▁ |
| Calcitonin | 0 | 1 | 1.68 | 0.87 | -0.71 | 0.96 | 1.65 | 2.28 | 3.89 | ▁▆▇▅▂ |
| CgA | 0 | 1 | 333.30 | 83.67 | 135.60 | 278.02 | 331.52 | 392.07 | 535.40 | ▂▆▇▅▂ |
| Clusterin_Apo_J | 0 | 1 | 2.88 | 0.29 | 1.87 | 2.71 | 2.89 | 3.04 | 3.58 | ▁▂▇▆▂ |
| Complement_3 | 0 | 1 | -15.61 | 2.46 | -23.39 | -17.57 | -15.52 | -13.88 | -9.56 | ▁▆▇▇▂ |
| Complement_Factor_H | 0 | 1 | 3.55 | 1.25 | -0.84 | 2.75 | 3.60 | 4.25 | 7.62 | ▁▃▇▅▁ |
| Connective_Tissue_Growth_Factor | 0 | 1 | 0.77 | 0.20 | 0.18 | 0.64 | 0.79 | 0.92 | 1.41 | ▁▅▇▃▁ |
| Cortisol | 0 | 1 | 11.98 | 3.95 | 0.10 | 9.80 | 12.00 | 14.00 | 29.00 | ▁▇▇▁▁ |
| Creatine_Kinase_MB | 0 | 1 | -1.67 | 0.09 | -1.87 | -1.72 | -1.67 | -1.63 | -1.38 | ▃▅▇▂▁ |
| Cystatin_C | 0 | 1 | 8.59 | 0.40 | 7.43 | 8.32 | 8.56 | 8.84 | 9.69 | ▁▅▇▃▁ |
| EGF_R | 0 | 1 | -0.70 | 0.22 | -1.36 | -0.86 | -0.68 | -0.55 | -0.06 | ▁▃▇▅▁ |
| EN_RAGE | 0 | 1 | -3.64 | 0.88 | -8.38 | -4.20 | -3.65 | -3.15 | -0.39 | ▁▁▇▆▁ |
| ENA_78 | 0 | 1 | -1.37 | 0.01 | -1.41 | -1.38 | -1.37 | -1.36 | -1.34 | ▁▅▇▅▁ |
| Eotaxin_3 | 0 | 1 | 58.17 | 15.83 | 7.00 | 44.00 | 59.00 | 70.00 | 107.00 | ▁▆▇▅▁ |
| FAS | 0 | 1 | -0.53 | 0.30 | -1.51 | -0.71 | -0.53 | -0.31 | 0.34 | ▁▃▇▆▁ |
| FSH_Follicle_Stimulation_Hormon | 0 | 1 | -1.14 | 0.42 | -2.12 | -1.47 | -1.14 | -0.88 | 0.10 | ▂▇▇▃▁ |
| Fas_Ligand | 0 | 1 | 2.97 | 1.09 | -0.15 | 2.34 | 3.10 | 3.70 | 7.63 | ▂▇▇▁▁ |
| Fatty_Acid_Binding_Protein | 0 | 1 | 1.35 | 0.78 | -1.04 | 0.80 | 1.39 | 1.88 | 3.71 | ▁▅▇▅▁ |
| Ferritin | 0 | 1 | 2.76 | 0.76 | 0.61 | 2.29 | 2.77 | 3.29 | 4.63 | ▁▃▇▆▂ |
| Fetuin_A | 0 | 1 | 1.35 | 0.37 | 0.47 | 1.10 | 1.31 | 1.61 | 2.25 | ▂▆▇▅▂ |
| Fibrinogen | 0 | 1 | -7.36 | 0.57 | -8.87 | -7.72 | -7.32 | -7.01 | -5.84 | ▁▅▇▅▁ |
| GRO_alpha | 0 | 1 | 1.38 | 0.04 | 1.27 | 1.35 | 1.38 | 1.41 | 1.49 | ▂▃▇▅▁ |
| Gamma_Interferon_induced_Monokin | 0 | 1 | 2.79 | 0.11 | 2.39 | 2.71 | 2.78 | 2.87 | 3.07 | ▁▂▇▆▂ |
| Glutathione_S_Transferase_alpha | 0 | 1 | 0.95 | 0.16 | 0.52 | 0.84 | 0.97 | 1.03 | 1.32 | ▁▃▇▅▂ |
| HB_EGF | 0 | 1 | 6.83 | 1.51 | 2.10 | 5.79 | 6.70 | 7.86 | 10.70 | ▁▃▇▆▂ |
| HCC_4 | 0 | 1 | -3.50 | 0.36 | -4.51 | -3.73 | -3.51 | -3.27 | -2.12 | ▂▇▇▂▁ |
| Hepatocyte_Growth_Factor_HGF | 0 | 1 | 0.20 | 0.29 | -0.63 | 0.00 | 0.18 | 0.41 | 0.88 | ▁▃▇▅▂ |
| I_309 | 0 | 1 | 2.96 | 0.37 | 1.76 | 2.71 | 2.94 | 3.22 | 4.14 | ▁▅▇▃▁ |
| ICAM_1 | 0 | 1 | -0.59 | 0.33 | -1.53 | -0.83 | -0.59 | -0.38 | 0.52 | ▁▅▇▃▁ |
| IGF_BP_2 | 0 | 1 | 5.32 | 0.20 | 4.63 | 5.18 | 5.32 | 5.45 | 5.95 | ▁▃▇▅▁ |
| IL_11 | 0 | 1 | 4.72 | 1.45 | 1.75 | 3.71 | 4.81 | 5.78 | 8.49 | ▃▆▇▅▁ |
| IL_13 | 0 | 1 | 1.28 | 0.01 | 1.26 | 1.27 | 1.28 | 1.29 | 1.32 | ▂▇▆▂▁ |
| IL_16 | 0 | 1 | 2.93 | 0.66 | 1.19 | 2.52 | 2.91 | 3.35 | 4.94 | ▁▆▇▃▁ |
| IL_17E | 0 | 1 | 4.85 | 1.34 | 1.05 | 4.15 | 4.75 | 5.63 | 8.95 | ▁▆▇▃▁ |
| IL_1alpha | 0 | 1 | -7.51 | 0.39 | -8.52 | -7.82 | -7.52 | -7.26 | -5.95 | ▂▇▇▂▁ |
| IL_3 | 0 | 1 | -3.94 | 0.50 | -5.91 | -4.27 | -3.91 | -3.63 | -2.45 | ▁▂▇▅▁ |
| IL_4 | 0 | 1 | 1.77 | 0.51 | 0.53 | 1.46 | 1.81 | 2.15 | 3.04 | ▂▅▇▅▂ |
| IL_5 | 0 | 1 | 0.19 | 0.46 | -1.43 | -0.12 | 0.18 | 0.47 | 1.95 | ▁▃▇▂▁ |
| IL_6 | 0 | 1 | -0.15 | 0.55 | -1.53 | -0.41 | -0.16 | 0.14 | 1.81 | ▂▆▇▂▁ |
| IL_6_Receptor | 0 | 1 | 0.09 | 0.32 | -0.68 | -0.13 | 0.10 | 0.35 | 0.83 | ▂▆▇▆▂ |
| IL_7 | 0 | 1 | 2.84 | 1.05 | 0.56 | 2.15 | 2.79 | 3.71 | 5.71 | ▃▇▇▆▁ |
| IL_8 | 0 | 1 | 1.70 | 0.04 | 1.57 | 1.68 | 1.71 | 1.73 | 1.81 | ▁▂▇▆▁ |
| IP_10_Inducible_Protein_10 | 0 | 1 | 5.75 | 0.51 | 4.32 | 5.40 | 5.75 | 6.06 | 7.50 | ▁▆▇▂▁ |
| IgA | 0 | 1 | -6.12 | 0.76 | -10.52 | -6.65 | -6.12 | -5.57 | -4.20 | ▁▁▂▇▂ |
| Insulin | 0 | 1 | -1.23 | 0.34 | -2.17 | -1.45 | -1.25 | -1.03 | -0.16 | ▁▃▇▂▁ |
| Kidney_Injury_Molecule_1_KIM_1 | 0 | 1 | -1.18 | 0.03 | -1.26 | -1.20 | -1.18 | -1.16 | -1.10 | ▁▇▇▅▁ |
| LOX_1 | 0 | 1 | 1.28 | 0.40 | 0.00 | 1.03 | 1.28 | 1.53 | 2.27 | ▁▂▇▅▂ |
| Leptin | 0 | 1 | -1.50 | 0.27 | -2.15 | -1.70 | -1.50 | -1.33 | -0.62 | ▂▇▇▂▁ |
| Lipoprotein_a | 0 | 1 | -4.42 | 1.11 | -6.81 | -5.31 | -4.61 | -3.49 | -1.39 | ▂▇▅▅▁ |
| MCP_1 | 0 | 1 | 6.50 | 0.26 | 5.83 | 6.32 | 6.49 | 6.68 | 7.23 | ▂▅▇▅▁ |
| MCP_2 | 0 | 1 | 1.87 | 0.65 | 0.40 | 1.53 | 1.85 | 2.18 | 4.02 | ▂▆▇▂▁ |
| MIF | 0 | 1 | -1.86 | 0.34 | -2.85 | -2.12 | -1.90 | -1.66 | -0.84 | ▁▃▇▃▁ |
| MIP_1alpha | 0 | 1 | 4.05 | 1.01 | 0.93 | 3.34 | 4.05 | 4.69 | 6.80 | ▁▅▇▅▂ |
| MIP_1beta | 0 | 1 | 2.81 | 0.38 | 1.95 | 2.56 | 2.83 | 3.04 | 4.01 | ▂▅▇▂▁ |
| MMP_2 | 0 | 1 | 2.88 | 0.93 | 0.10 | 2.33 | 2.82 | 3.55 | 5.36 | ▁▃▇▆▁ |
| MMP_3 | 0 | 1 | -2.45 | 0.57 | -4.42 | -2.75 | -2.45 | -2.12 | -0.53 | ▁▂▇▃▁ |
| MMP10 | 0 | 1 | -3.63 | 0.43 | -4.93 | -3.94 | -3.65 | -3.35 | -2.21 | ▁▆▇▃▁ |
| MMP7 | 0 | 1 | -3.79 | 1.55 | -8.40 | -4.82 | -3.77 | -2.71 | -0.22 | ▁▃▇▅▂ |
| Myoglobin | 0 | 1 | -1.37 | 0.95 | -3.17 | -2.04 | -1.47 | -0.80 | 1.77 | ▅▇▆▂▁ |
| NT_proBNP | 0 | 1 | 4.55 | 0.38 | 3.18 | 4.35 | 4.55 | 4.77 | 5.89 | ▁▂▇▃▁ |
| NrCAM | 0 | 1 | 4.36 | 0.57 | 2.64 | 4.00 | 4.39 | 4.75 | 6.01 | ▁▃▇▅▁ |
| Osteopontin | 0 | 1 | 5.20 | 0.39 | 4.11 | 4.96 | 5.19 | 5.44 | 6.31 | ▁▅▇▃▁ |
| PAI_1 | 0 | 1 | 0.08 | 0.41 | -0.99 | -0.17 | 0.09 | 0.32 | 1.17 | ▂▃▇▅▂ |
| PAPP_A | 0 | 1 | -2.85 | 0.14 | -3.31 | -2.94 | -2.87 | -2.75 | -2.52 | ▁▂▇▅▂ |
| PLGF | 0 | 1 | 3.91 | 0.41 | 2.48 | 3.64 | 3.87 | 4.20 | 5.17 | ▁▃▇▆▁ |
| PYY | 0 | 1 | 3.02 | 0.29 | 2.19 | 2.83 | 3.00 | 3.18 | 3.93 | ▁▅▇▃▁ |
| Pancreatic_polypeptide | 0 | 1 | -0.01 | 0.72 | -2.12 | -0.53 | -0.04 | 0.53 | 1.93 | ▁▅▇▆▁ |
| Prolactin | 0 | 1 | 0.04 | 0.30 | -1.31 | -0.14 | 0.00 | 0.26 | 0.99 | ▁▁▇▇▁ |
| Prostatic_Acid_Phosphatase | 0 | 1 | -1.69 | 0.06 | -1.93 | -1.72 | -1.69 | -1.65 | -1.42 | ▁▂▇▂▁ |
| Protein_S | 0 | 1 | -2.24 | 0.35 | -3.34 | -2.46 | -2.26 | -2.00 | -1.22 | ▁▃▇▃▁ |
| Pulmonary_and_Activation_Regulat | 0 | 1 | -1.49 | 0.45 | -2.51 | -1.83 | -1.51 | -1.17 | -0.27 | ▂▇▇▃▂ |
| RANTES | 0 | 1 | -6.51 | 0.32 | -7.22 | -6.73 | -6.50 | -6.32 | -5.55 | ▃▇▇▃▁ |
| Resistin | 0 | 1 | -17.64 | 5.82 | -34.97 | -21.47 | -17.47 | -13.50 | -2.24 | ▁▃▇▆▁ |
| S100b | 0 | 1 | 1.25 | 0.34 | 0.19 | 1.00 | 1.25 | 1.50 | 2.37 | ▁▆▇▅▁ |
| SGOT | 0 | 1 | -0.41 | 0.35 | -1.35 | -0.63 | -0.40 | -0.20 | 0.74 | ▁▅▇▂▁ |
| SHBG | 0 | 1 | -2.48 | 0.58 | -4.14 | -2.81 | -2.49 | -2.12 | -1.11 | ▁▃▇▆▂ |
| SOD | 0 | 1 | 5.34 | 0.38 | 4.32 | 5.09 | 5.37 | 5.58 | 6.32 | ▁▅▇▅▁ |
| Serum_Amyloid_P | 0 | 1 | -6.02 | 0.56 | -7.51 | -6.38 | -6.03 | -5.65 | -4.65 | ▁▅▇▅▂ |
| Sortilin | 0 | 1 | 3.85 | 0.87 | 1.65 | 3.34 | 3.87 | 4.37 | 6.23 | ▁▅▇▃▁ |
| Stem_Cell_Factor | 0 | 1 | 3.30 | 0.36 | 2.25 | 3.04 | 3.30 | 3.53 | 4.28 | ▁▅▇▅▁ |
| TGF_alpha | 0 | 1 | 9.80 | 1.32 | 6.84 | 8.86 | 9.92 | 10.70 | 13.83 | ▂▆▇▂▁ |
| TIMP_1 | 0 | 1 | 11.75 | 1.90 | 1.74 | 10.49 | 11.56 | 12.70 | 18.88 | ▁▁▇▅▁ |
| TNF_RII | 0 | 1 | -0.59 | 0.33 | -1.66 | -0.82 | -0.60 | -0.38 | 0.47 | ▁▃▇▃▁ |
| TRAIL_R3 | 0 | 1 | -0.54 | 0.24 | -1.21 | -0.70 | -0.53 | -0.38 | 0.27 | ▁▆▇▂▁ |
| TTR_prealbumin | 0 | 1 | 2.85 | 0.14 | 2.48 | 2.77 | 2.83 | 2.94 | 3.33 | ▂▅▇▃▁ |
| Tamm_Horsfall_Protein_THP | 0 | 1 | -3.12 | 0.03 | -3.21 | -3.14 | -3.12 | -3.10 | -2.99 | ▁▇▇▂▁ |
| Thrombomodulin | 0 | 1 | -1.51 | 0.22 | -2.04 | -1.63 | -1.49 | -1.34 | -0.82 | ▂▆▇▃▁ |
| Thrombopoietin | 0 | 1 | -0.75 | 0.24 | -1.54 | -0.89 | -0.75 | -0.63 | 0.10 | ▁▅▇▂▁ |
| Thymus_Expressed_Chemokine_TECK | 0 | 1 | 3.85 | 0.80 | 1.51 | 3.34 | 3.81 | 4.32 | 6.23 | ▁▃▇▃▁ |
| Thyroid_Stimulating_Hormone | 0 | 1 | -4.50 | 0.75 | -6.19 | -4.96 | -4.51 | -4.02 | -1.71 | ▂▇▆▁▁ |
| Thyroxine_Binding_Globulin | 0 | 1 | -1.48 | 0.40 | -2.48 | -1.77 | -1.51 | -1.24 | -0.21 | ▂▇▇▃▁ |
| Tissue_Factor | 0 | 1 | 1.17 | 0.50 | -0.21 | 0.83 | 1.22 | 1.48 | 2.48 | ▁▅▇▆▁ |
| Transferrin | 0 | 1 | 2.91 | 0.27 | 1.93 | 2.71 | 2.89 | 3.09 | 3.76 | ▁▂▇▅▁ |
| Trefoil_Factor_3_TFF3 | 0 | 1 | -3.88 | 0.34 | -4.74 | -4.14 | -3.86 | -3.65 | -2.96 | ▁▅▇▃▁ |
| VCAM_1 | 0 | 1 | 2.69 | 0.32 | 1.72 | 2.48 | 2.71 | 2.89 | 3.69 | ▁▃▇▃▁ |
| VEGF | 0 | 1 | 16.99 | 1.81 | 11.83 | 15.77 | 17.08 | 18.10 | 22.38 | ▁▅▇▃▁ |
| Vitronectin | 0 | 1 | -0.28 | 0.33 | -1.43 | -0.51 | -0.30 | -0.04 | 0.53 | ▁▃▇▇▂ |
| von_Willebrand_Factor | 0 | 1 | -3.91 | 0.38 | -4.99 | -4.20 | -3.91 | -3.61 | -2.96 | ▁▅▇▆▂ |
| E4 | 0 | 1 | 0.40 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| E3 | 0 | 1 | 0.92 | 0.28 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▁▁▇ |
| E2 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 95/5,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## [1] freqRatio percentUnique zeroVar nzv
## <0 rows> (or 0-length row.names)if ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance predictors noted.")
} else {
print(paste0("Low variance observed for ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
print(paste0("Low variance can be resolved by removing ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s)."))
for (j in 1:DPA_LowVarianceForRemoval) {
DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LowVarianceRemovedVariable))
}
DPA %>%
skim() %>%
dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))
##################################
# Filtering out columns with low variance
#################################
DPA_ExcludedLowVariance <- DPA[,!names(DPA) %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,])]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedLowVariance_Skimmed <- skim(DPA_ExcludedLowVariance))
}## [1] "No low variance predictors noted."1.3.3 Collinearity
High collinearity data assessment:
[A] No high correlation > 95% were noted for any variable pair as confirmed using the preprocessing summaries from the caret and lares packages.
##################################
# Loading dataset
##################################
DPA <- Alzheimer_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,!names(DPA.Predictors) %in% c("E2","E3","E4")]
DPA.Predictors.Numeric <- as.data.frame(sapply(DPA.Predictors.Numeric,function(x) as.numeric(x)))
##################################
# Visualizing pairwise correlation between predictors
##################################
DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
method = "pearson",
conf.level = .95)
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "circle",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")
##################################
# Identifying the highly correlated variables
##################################
DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs")
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)]) > 0.95))## [1] 0if (DPA_HighlyCorrelatedCount == 0) {
print("No highly correlated predictors noted.")
} else {
print(paste0("High correlation observed for ",
(DPA_HighlyCorrelatedCount),
" pairs of numeric variable(s) with Correlation.Coefficient>0.95."))
(DPA_HighlyCorrelatedPairs <- corr_cross(DPA.Predictors.Numeric,
max_pvalue = 0.05,
top = DPA_HighlyCorrelatedCount,
rm.na = TRUE,
grid = FALSE
))
}## [1] "No highly correlated predictors noted."if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.95)
(DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
print(paste0("High correlation can be resolved by removing ",
(DPA_HighlyCorrelatedForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_HighlyCorrelatedForRemoval) {
DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_HighlyCorrelatedRemovedVariable))
}
##################################
# Filtering out columns with high correlation
#################################
DPA_ExcludedHighCorrelation <- DPA[,-DPA_HighlyCorrelated]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedHighCorrelation_Skimmed <- skim(DPA_ExcludedHighCorrelation))
}1.3.4 Linear Dependencies
Linear dependency data assessment:
[A] No linear dependencies noted for any subset of variables using the preprocessing summary from the caret package applying the findLinearCombos method which utilizes the QR decomposition of a matrix to enumerate sets of linear combinations (if they exist).
##################################
# Loading dataset
##################################
DPA <- Alzheimer_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,!names(DPA.Predictors) %in% c("E2","E3","E4")]
DPA.Predictors.Numeric <- as.data.frame(sapply(DPA.Predictors.Numeric,function(x) as.numeric(x)))
##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))## [1] 0if (DPA_LinearlyDependentCount == 0) {
print("No linearly dependent predictors noted.")
} else {
print(paste0("Linear dependency observed for ",
(DPA_LinearlyDependentCount),
" subset(s) of numeric variable(s)."))
for (i in 1:DPA_LinearlyDependentCount) {
DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
print(paste0("Linear dependent variable(s) for subset ",
i,
" include: ",
DPA_LinearlyDependentSubset))
}
}## [1] "No linearly dependent predictors noted."##################################
# Identifying the linearly dependent variables for removal
##################################
if (DPA_LinearlyDependentCount > 0) {
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
print(paste0("Linear dependency can be resolved by removing ",
(DPA_LinearlyDependentForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_LinearlyDependentForRemoval) {
DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LinearlyDependentRemovedVariable))
}
##################################
# Filtering out columns with linear dependency
#################################
DPA_ExcludedLinearlyDependent <- DPA[,-DPA_LinearlyDependent$remove]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedLinearlyDependent_Skimmed <- skim(DPA_ExcludedLinearlyDependent))
}1.3.5 Pre-Processed Dataset
Preliminary dataset assessment:
[A] 333 rows (observations)
[A.1] Train Set = 267 observations
[A.2] Test Set = 66 observations
[B] 128 columns (variables)
[B.1] 1/128 response = Class variable (factor)
[B.1.1] Levels = Class=Control < Class=Impaired
[B.2] 127/128 predictors = All remaining variables (3/127 factor + 124/127 numeric)
[C] No pre-processing actions applied:
[C.1] No shape transformation applied since distributions were fairly normal
[C.2] Centering and scaling may be necessary due to the differences in ranges for the numeric variables however, these will be selectively applied based on model requirements
[C.2] No outlier treatment applied since the high values noted were minimal and contextually valid
[C.3] No predictors removed due to zero or near-zero variance
[C.4] No predictors removed due to high correlation
[C.5] No predictors removed due to linear dependencies
[A] 333 rows (observations)
[A.1] Train Set = 267 observations
[A.2] Test Set = 66 observations
[B] 128 columns (variables)
[B.1] 1/128 response = Class variable (factor)
[B.1.1] Levels = Class=Control < Class=Impaired
[B.2] 127/128 predictors = All remaining variables (3/127 factor + 124/127 numeric)
[C] No pre-processing actions applied:
[C.1] No shape transformation applied since distributions were fairly normal
[C.2] Centering and scaling may be necessary due to the differences in ranges for the numeric variables however, these will be selectively applied based on model requirements
[C.2] No outlier treatment applied since the high values noted were minimal and contextually valid
[C.3] No predictors removed due to zero or near-zero variance
[C.4] No predictors removed due to high correlation
[C.5] No predictors removed due to linear dependencies
##################################
# Creating the pre-modelling
# train set
##################################
PMA_PreModelling_Train <- Alzheimer_Train
PMA_PreModelling_Train$Class <- as.factor(PMA_PreModelling_Train$Class)
PMA_PreModelling_Train$E2 <- as.factor(PMA_PreModelling_Train$E2)
PMA_PreModelling_Train$E3 <- as.factor(PMA_PreModelling_Train$E3)
PMA_PreModelling_Train$E4 <- as.factor(PMA_PreModelling_Train$E4)
##################################
# Gathering descriptive statistics
##################################
(PMA_PreModelling_Train_Skimmed <- skim(PMA_PreModelling_Train))| Name | PMA_PreModelling_Train |
| Number of rows | 267 |
| Number of columns | 128 |
| _______________________ | |
| Column type frequency: | |
| factor | 4 |
| numeric | 124 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Class | 0 | 1 | FALSE | 2 | Con: 194, Imp: 73 |
| E4 | 0 | 1 | FALSE | 2 | 0: 160, 1: 107 |
| E3 | 0 | 1 | FALSE | 2 | 1: 245, 0: 22 |
| E2 | 0 | 1 | FALSE | 2 | 0: 224, 1: 43 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ACE_CD143_Angiotensin_Converti | 0 | 1 | 1.32 | 0.54 | -0.68 | 0.95 | 1.30 | 1.72 | 2.84 | ▁▃▇▇▁ |
| ACTH_Adrenocorticotropic_Hormon | 0 | 1 | -1.54 | 0.28 | -2.21 | -1.71 | -1.56 | -1.35 | -0.84 | ▂▆▇▆▂ |
| AXL | 0 | 1 | 0.31 | 0.45 | -0.92 | 0.00 | 0.28 | 0.61 | 1.52 | ▁▅▇▃▁ |
| Adiponectin | 0 | 1 | -5.20 | 0.67 | -6.73 | -5.67 | -5.18 | -4.78 | -3.51 | ▂▆▇▅▁ |
| Alpha_1_Antichymotrypsin | 0 | 1 | 1.36 | 0.36 | 0.26 | 1.13 | 1.36 | 1.59 | 2.30 | ▁▃▇▅▁ |
| Alpha_1_Antitrypsin | 0 | 1 | -13.05 | 1.48 | -17.03 | -14.07 | -13.00 | -12.10 | -8.19 | ▁▅▇▂▁ |
| Alpha_1_Microglobulin | 0 | 1 | -2.93 | 0.48 | -4.34 | -3.27 | -2.94 | -2.59 | -1.77 | ▁▅▇▇▂ |
| Alpha_2_Macroglobulin | 0 | 1 | -158.61 | 40.93 | -289.68 | -186.64 | -160.01 | -134.62 | -59.46 | ▁▂▇▅▂ |
| Angiopoietin_2_ANG_2 | 0 | 1 | 0.67 | 0.33 | -0.54 | 0.47 | 0.64 | 0.88 | 1.53 | ▁▂▇▆▂ |
| Angiotensinogen | 0 | 1 | 2.32 | 0.25 | 1.75 | 2.12 | 2.32 | 2.50 | 2.88 | ▃▆▇▇▂ |
| Apolipoprotein_A_IV | 0 | 1 | -1.85 | 0.38 | -2.96 | -2.12 | -1.83 | -1.61 | -0.78 | ▁▃▇▃▁ |
| Apolipoprotein_A1 | 0 | 1 | -7.48 | 0.44 | -8.68 | -7.76 | -7.47 | -7.21 | -6.17 | ▁▅▇▃▁ |
| Apolipoprotein_A2 | 0 | 1 | -0.64 | 0.50 | -1.90 | -0.97 | -0.67 | -0.31 | 0.96 | ▁▇▇▃▁ |
| Apolipoprotein_B | 0 | 1 | -5.58 | 1.48 | -9.94 | -6.63 | -5.70 | -4.54 | -2.15 | ▁▅▇▇▂ |
| Apolipoprotein_CI | 0 | 1 | -1.58 | 0.41 | -3.32 | -1.83 | -1.61 | -1.37 | -0.27 | ▁▁▇▅▁ |
| Apolipoprotein_CIII | 0 | 1 | -2.49 | 0.44 | -3.69 | -2.77 | -2.53 | -2.21 | -1.24 | ▁▅▇▅▁ |
| Apolipoprotein_D | 0 | 1 | 1.44 | 0.34 | 0.47 | 1.21 | 1.41 | 1.67 | 2.27 | ▁▃▇▆▂ |
| Apolipoprotein_E | 0 | 1 | 2.81 | 0.78 | 0.59 | 2.33 | 2.82 | 3.29 | 5.44 | ▁▅▇▂▁ |
| Apolipoprotein_H | 0 | 1 | -0.32 | 0.39 | -2.23 | -0.60 | -0.37 | -0.06 | 0.93 | ▁▁▇▆▁ |
| B_Lymphocyte_Chemoattractant_BL | 0 | 1 | 2.02 | 0.57 | 0.73 | 1.67 | 1.98 | 2.37 | 4.02 | ▃▇▇▁▁ |
| BMP_6 | 0 | 1 | -1.91 | 0.31 | -2.76 | -2.15 | -1.88 | -1.68 | -0.82 | ▁▅▇▂▁ |
| Beta_2_Microglobulin | 0 | 1 | 0.17 | 0.30 | -0.54 | -0.04 | 0.18 | 0.34 | 0.99 | ▂▃▇▃▁ |
| Betacellulin | 0 | 1 | 51.01 | 10.87 | 10.00 | 42.00 | 51.00 | 59.00 | 82.00 | ▁▁▇▅▁ |
| C_Reactive_Protein | 0 | 1 | -5.87 | 1.20 | -8.52 | -6.65 | -5.84 | -5.08 | -2.94 | ▃▆▇▆▂ |
| CD40 | 0 | 1 | -1.26 | 0.21 | -1.86 | -1.38 | -1.27 | -1.12 | -0.55 | ▁▆▇▃▁ |
| CD5L | 0 | 1 | -0.05 | 0.45 | -1.24 | -0.36 | -0.06 | 0.26 | 1.16 | ▁▅▇▃▁ |
| Calbindin | 0 | 1 | 22.43 | 4.11 | 10.96 | 19.77 | 22.25 | 24.80 | 33.78 | ▁▃▇▃▁ |
| Calcitonin | 0 | 1 | 1.68 | 0.87 | -0.71 | 0.96 | 1.65 | 2.28 | 3.89 | ▁▆▇▅▂ |
| CgA | 0 | 1 | 333.30 | 83.67 | 135.60 | 278.02 | 331.52 | 392.07 | 535.40 | ▂▆▇▅▂ |
| Clusterin_Apo_J | 0 | 1 | 2.88 | 0.29 | 1.87 | 2.71 | 2.89 | 3.04 | 3.58 | ▁▂▇▆▂ |
| Complement_3 | 0 | 1 | -15.61 | 2.46 | -23.39 | -17.57 | -15.52 | -13.88 | -9.56 | ▁▆▇▇▂ |
| Complement_Factor_H | 0 | 1 | 3.55 | 1.25 | -0.84 | 2.75 | 3.60 | 4.25 | 7.62 | ▁▃▇▅▁ |
| Connective_Tissue_Growth_Factor | 0 | 1 | 0.77 | 0.20 | 0.18 | 0.64 | 0.79 | 0.92 | 1.41 | ▁▅▇▃▁ |
| Cortisol | 0 | 1 | 11.98 | 3.95 | 0.10 | 9.80 | 12.00 | 14.00 | 29.00 | ▁▇▇▁▁ |
| Creatine_Kinase_MB | 0 | 1 | -1.67 | 0.09 | -1.87 | -1.72 | -1.67 | -1.63 | -1.38 | ▃▅▇▂▁ |
| Cystatin_C | 0 | 1 | 8.59 | 0.40 | 7.43 | 8.32 | 8.56 | 8.84 | 9.69 | ▁▅▇▃▁ |
| EGF_R | 0 | 1 | -0.70 | 0.22 | -1.36 | -0.86 | -0.68 | -0.55 | -0.06 | ▁▃▇▅▁ |
| EN_RAGE | 0 | 1 | -3.64 | 0.88 | -8.38 | -4.20 | -3.65 | -3.15 | -0.39 | ▁▁▇▆▁ |
| ENA_78 | 0 | 1 | -1.37 | 0.01 | -1.41 | -1.38 | -1.37 | -1.36 | -1.34 | ▁▅▇▅▁ |
| Eotaxin_3 | 0 | 1 | 58.17 | 15.83 | 7.00 | 44.00 | 59.00 | 70.00 | 107.00 | ▁▆▇▅▁ |
| FAS | 0 | 1 | -0.53 | 0.30 | -1.51 | -0.71 | -0.53 | -0.31 | 0.34 | ▁▃▇▆▁ |
| FSH_Follicle_Stimulation_Hormon | 0 | 1 | -1.14 | 0.42 | -2.12 | -1.47 | -1.14 | -0.88 | 0.10 | ▂▇▇▃▁ |
| Fas_Ligand | 0 | 1 | 2.97 | 1.09 | -0.15 | 2.34 | 3.10 | 3.70 | 7.63 | ▂▇▇▁▁ |
| Fatty_Acid_Binding_Protein | 0 | 1 | 1.35 | 0.78 | -1.04 | 0.80 | 1.39 | 1.88 | 3.71 | ▁▅▇▅▁ |
| Ferritin | 0 | 1 | 2.76 | 0.76 | 0.61 | 2.29 | 2.77 | 3.29 | 4.63 | ▁▃▇▆▂ |
| Fetuin_A | 0 | 1 | 1.35 | 0.37 | 0.47 | 1.10 | 1.31 | 1.61 | 2.25 | ▂▆▇▅▂ |
| Fibrinogen | 0 | 1 | -7.36 | 0.57 | -8.87 | -7.72 | -7.32 | -7.01 | -5.84 | ▁▅▇▅▁ |
| GRO_alpha | 0 | 1 | 1.38 | 0.04 | 1.27 | 1.35 | 1.38 | 1.41 | 1.49 | ▂▃▇▅▁ |
| Gamma_Interferon_induced_Monokin | 0 | 1 | 2.79 | 0.11 | 2.39 | 2.71 | 2.78 | 2.87 | 3.07 | ▁▂▇▆▂ |
| Glutathione_S_Transferase_alpha | 0 | 1 | 0.95 | 0.16 | 0.52 | 0.84 | 0.97 | 1.03 | 1.32 | ▁▃▇▅▂ |
| HB_EGF | 0 | 1 | 6.83 | 1.51 | 2.10 | 5.79 | 6.70 | 7.86 | 10.70 | ▁▃▇▆▂ |
| HCC_4 | 0 | 1 | -3.50 | 0.36 | -4.51 | -3.73 | -3.51 | -3.27 | -2.12 | ▂▇▇▂▁ |
| Hepatocyte_Growth_Factor_HGF | 0 | 1 | 0.20 | 0.29 | -0.63 | 0.00 | 0.18 | 0.41 | 0.88 | ▁▃▇▅▂ |
| I_309 | 0 | 1 | 2.96 | 0.37 | 1.76 | 2.71 | 2.94 | 3.22 | 4.14 | ▁▅▇▃▁ |
| ICAM_1 | 0 | 1 | -0.59 | 0.33 | -1.53 | -0.83 | -0.59 | -0.38 | 0.52 | ▁▅▇▃▁ |
| IGF_BP_2 | 0 | 1 | 5.32 | 0.20 | 4.63 | 5.18 | 5.32 | 5.45 | 5.95 | ▁▃▇▅▁ |
| IL_11 | 0 | 1 | 4.72 | 1.45 | 1.75 | 3.71 | 4.81 | 5.78 | 8.49 | ▃▆▇▅▁ |
| IL_13 | 0 | 1 | 1.28 | 0.01 | 1.26 | 1.27 | 1.28 | 1.29 | 1.32 | ▂▇▆▂▁ |
| IL_16 | 0 | 1 | 2.93 | 0.66 | 1.19 | 2.52 | 2.91 | 3.35 | 4.94 | ▁▆▇▃▁ |
| IL_17E | 0 | 1 | 4.85 | 1.34 | 1.05 | 4.15 | 4.75 | 5.63 | 8.95 | ▁▆▇▃▁ |
| IL_1alpha | 0 | 1 | -7.51 | 0.39 | -8.52 | -7.82 | -7.52 | -7.26 | -5.95 | ▂▇▇▂▁ |
| IL_3 | 0 | 1 | -3.94 | 0.50 | -5.91 | -4.27 | -3.91 | -3.63 | -2.45 | ▁▂▇▅▁ |
| IL_4 | 0 | 1 | 1.77 | 0.51 | 0.53 | 1.46 | 1.81 | 2.15 | 3.04 | ▂▅▇▅▂ |
| IL_5 | 0 | 1 | 0.19 | 0.46 | -1.43 | -0.12 | 0.18 | 0.47 | 1.95 | ▁▃▇▂▁ |
| IL_6 | 0 | 1 | -0.15 | 0.55 | -1.53 | -0.41 | -0.16 | 0.14 | 1.81 | ▂▆▇▂▁ |
| IL_6_Receptor | 0 | 1 | 0.09 | 0.32 | -0.68 | -0.13 | 0.10 | 0.35 | 0.83 | ▂▆▇▆▂ |
| IL_7 | 0 | 1 | 2.84 | 1.05 | 0.56 | 2.15 | 2.79 | 3.71 | 5.71 | ▃▇▇▆▁ |
| IL_8 | 0 | 1 | 1.70 | 0.04 | 1.57 | 1.68 | 1.71 | 1.73 | 1.81 | ▁▂▇▆▁ |
| IP_10_Inducible_Protein_10 | 0 | 1 | 5.75 | 0.51 | 4.32 | 5.40 | 5.75 | 6.06 | 7.50 | ▁▆▇▂▁ |
| IgA | 0 | 1 | -6.12 | 0.76 | -10.52 | -6.65 | -6.12 | -5.57 | -4.20 | ▁▁▂▇▂ |
| Insulin | 0 | 1 | -1.23 | 0.34 | -2.17 | -1.45 | -1.25 | -1.03 | -0.16 | ▁▃▇▂▁ |
| Kidney_Injury_Molecule_1_KIM_1 | 0 | 1 | -1.18 | 0.03 | -1.26 | -1.20 | -1.18 | -1.16 | -1.10 | ▁▇▇▅▁ |
| LOX_1 | 0 | 1 | 1.28 | 0.40 | 0.00 | 1.03 | 1.28 | 1.53 | 2.27 | ▁▂▇▅▂ |
| Leptin | 0 | 1 | -1.50 | 0.27 | -2.15 | -1.70 | -1.50 | -1.33 | -0.62 | ▂▇▇▂▁ |
| Lipoprotein_a | 0 | 1 | -4.42 | 1.11 | -6.81 | -5.31 | -4.61 | -3.49 | -1.39 | ▂▇▅▅▁ |
| MCP_1 | 0 | 1 | 6.50 | 0.26 | 5.83 | 6.32 | 6.49 | 6.68 | 7.23 | ▂▅▇▅▁ |
| MCP_2 | 0 | 1 | 1.87 | 0.65 | 0.40 | 1.53 | 1.85 | 2.18 | 4.02 | ▂▆▇▂▁ |
| MIF | 0 | 1 | -1.86 | 0.34 | -2.85 | -2.12 | -1.90 | -1.66 | -0.84 | ▁▃▇▃▁ |
| MIP_1alpha | 0 | 1 | 4.05 | 1.01 | 0.93 | 3.34 | 4.05 | 4.69 | 6.80 | ▁▅▇▅▂ |
| MIP_1beta | 0 | 1 | 2.81 | 0.38 | 1.95 | 2.56 | 2.83 | 3.04 | 4.01 | ▂▅▇▂▁ |
| MMP_2 | 0 | 1 | 2.88 | 0.93 | 0.10 | 2.33 | 2.82 | 3.55 | 5.36 | ▁▃▇▆▁ |
| MMP_3 | 0 | 1 | -2.45 | 0.57 | -4.42 | -2.75 | -2.45 | -2.12 | -0.53 | ▁▂▇▃▁ |
| MMP10 | 0 | 1 | -3.63 | 0.43 | -4.93 | -3.94 | -3.65 | -3.35 | -2.21 | ▁▆▇▃▁ |
| MMP7 | 0 | 1 | -3.79 | 1.55 | -8.40 | -4.82 | -3.77 | -2.71 | -0.22 | ▁▃▇▅▂ |
| Myoglobin | 0 | 1 | -1.37 | 0.95 | -3.17 | -2.04 | -1.47 | -0.80 | 1.77 | ▅▇▆▂▁ |
| NT_proBNP | 0 | 1 | 4.55 | 0.38 | 3.18 | 4.35 | 4.55 | 4.77 | 5.89 | ▁▂▇▃▁ |
| NrCAM | 0 | 1 | 4.36 | 0.57 | 2.64 | 4.00 | 4.39 | 4.75 | 6.01 | ▁▃▇▅▁ |
| Osteopontin | 0 | 1 | 5.20 | 0.39 | 4.11 | 4.96 | 5.19 | 5.44 | 6.31 | ▁▅▇▃▁ |
| PAI_1 | 0 | 1 | 0.08 | 0.41 | -0.99 | -0.17 | 0.09 | 0.32 | 1.17 | ▂▃▇▅▂ |
| PAPP_A | 0 | 1 | -2.85 | 0.14 | -3.31 | -2.94 | -2.87 | -2.75 | -2.52 | ▁▂▇▅▂ |
| PLGF | 0 | 1 | 3.91 | 0.41 | 2.48 | 3.64 | 3.87 | 4.20 | 5.17 | ▁▃▇▆▁ |
| PYY | 0 | 1 | 3.02 | 0.29 | 2.19 | 2.83 | 3.00 | 3.18 | 3.93 | ▁▅▇▃▁ |
| Pancreatic_polypeptide | 0 | 1 | -0.01 | 0.72 | -2.12 | -0.53 | -0.04 | 0.53 | 1.93 | ▁▅▇▆▁ |
| Prolactin | 0 | 1 | 0.04 | 0.30 | -1.31 | -0.14 | 0.00 | 0.26 | 0.99 | ▁▁▇▇▁ |
| Prostatic_Acid_Phosphatase | 0 | 1 | -1.69 | 0.06 | -1.93 | -1.72 | -1.69 | -1.65 | -1.42 | ▁▂▇▂▁ |
| Protein_S | 0 | 1 | -2.24 | 0.35 | -3.34 | -2.46 | -2.26 | -2.00 | -1.22 | ▁▃▇▃▁ |
| Pulmonary_and_Activation_Regulat | 0 | 1 | -1.49 | 0.45 | -2.51 | -1.83 | -1.51 | -1.17 | -0.27 | ▂▇▇▃▂ |
| RANTES | 0 | 1 | -6.51 | 0.32 | -7.22 | -6.73 | -6.50 | -6.32 | -5.55 | ▃▇▇▃▁ |
| Resistin | 0 | 1 | -17.64 | 5.82 | -34.97 | -21.47 | -17.47 | -13.50 | -2.24 | ▁▃▇▆▁ |
| S100b | 0 | 1 | 1.25 | 0.34 | 0.19 | 1.00 | 1.25 | 1.50 | 2.37 | ▁▆▇▅▁ |
| SGOT | 0 | 1 | -0.41 | 0.35 | -1.35 | -0.63 | -0.40 | -0.20 | 0.74 | ▁▅▇▂▁ |
| SHBG | 0 | 1 | -2.48 | 0.58 | -4.14 | -2.81 | -2.49 | -2.12 | -1.11 | ▁▃▇▆▂ |
| SOD | 0 | 1 | 5.34 | 0.38 | 4.32 | 5.09 | 5.37 | 5.58 | 6.32 | ▁▅▇▅▁ |
| Serum_Amyloid_P | 0 | 1 | -6.02 | 0.56 | -7.51 | -6.38 | -6.03 | -5.65 | -4.65 | ▁▅▇▅▂ |
| Sortilin | 0 | 1 | 3.85 | 0.87 | 1.65 | 3.34 | 3.87 | 4.37 | 6.23 | ▁▅▇▃▁ |
| Stem_Cell_Factor | 0 | 1 | 3.30 | 0.36 | 2.25 | 3.04 | 3.30 | 3.53 | 4.28 | ▁▅▇▅▁ |
| TGF_alpha | 0 | 1 | 9.80 | 1.32 | 6.84 | 8.86 | 9.92 | 10.70 | 13.83 | ▂▆▇▂▁ |
| TIMP_1 | 0 | 1 | 11.75 | 1.90 | 1.74 | 10.49 | 11.56 | 12.70 | 18.88 | ▁▁▇▅▁ |
| TNF_RII | 0 | 1 | -0.59 | 0.33 | -1.66 | -0.82 | -0.60 | -0.38 | 0.47 | ▁▃▇▃▁ |
| TRAIL_R3 | 0 | 1 | -0.54 | 0.24 | -1.21 | -0.70 | -0.53 | -0.38 | 0.27 | ▁▆▇▂▁ |
| TTR_prealbumin | 0 | 1 | 2.85 | 0.14 | 2.48 | 2.77 | 2.83 | 2.94 | 3.33 | ▂▅▇▃▁ |
| Tamm_Horsfall_Protein_THP | 0 | 1 | -3.12 | 0.03 | -3.21 | -3.14 | -3.12 | -3.10 | -2.99 | ▁▇▇▂▁ |
| Thrombomodulin | 0 | 1 | -1.51 | 0.22 | -2.04 | -1.63 | -1.49 | -1.34 | -0.82 | ▂▆▇▃▁ |
| Thrombopoietin | 0 | 1 | -0.75 | 0.24 | -1.54 | -0.89 | -0.75 | -0.63 | 0.10 | ▁▅▇▂▁ |
| Thymus_Expressed_Chemokine_TECK | 0 | 1 | 3.85 | 0.80 | 1.51 | 3.34 | 3.81 | 4.32 | 6.23 | ▁▃▇▃▁ |
| Thyroid_Stimulating_Hormone | 0 | 1 | -4.50 | 0.75 | -6.19 | -4.96 | -4.51 | -4.02 | -1.71 | ▂▇▆▁▁ |
| Thyroxine_Binding_Globulin | 0 | 1 | -1.48 | 0.40 | -2.48 | -1.77 | -1.51 | -1.24 | -0.21 | ▂▇▇▃▁ |
| Tissue_Factor | 0 | 1 | 1.17 | 0.50 | -0.21 | 0.83 | 1.22 | 1.48 | 2.48 | ▁▅▇▆▁ |
| Transferrin | 0 | 1 | 2.91 | 0.27 | 1.93 | 2.71 | 2.89 | 3.09 | 3.76 | ▁▂▇▅▁ |
| Trefoil_Factor_3_TFF3 | 0 | 1 | -3.88 | 0.34 | -4.74 | -4.14 | -3.86 | -3.65 | -2.96 | ▁▅▇▃▁ |
| VCAM_1 | 0 | 1 | 2.69 | 0.32 | 1.72 | 2.48 | 2.71 | 2.89 | 3.69 | ▁▃▇▃▁ |
| VEGF | 0 | 1 | 16.99 | 1.81 | 11.83 | 15.77 | 17.08 | 18.10 | 22.38 | ▁▅▇▃▁ |
| Vitronectin | 0 | 1 | -0.28 | 0.33 | -1.43 | -0.51 | -0.30 | -0.04 | 0.53 | ▁▃▇▇▂ |
| von_Willebrand_Factor | 0 | 1 | -3.91 | 0.38 | -4.99 | -4.20 | -3.91 | -3.61 | -2.96 | ▁▅▇▆▂ |
###################################
# Verifying the data dimensions
# for the train set
###################################
dim(PMA_PreModelling_Train)## [1] 267 128##################################
# Formulating the test set
##################################
PMA_PreModelling_Test <- Alzheimer_Test
PMA_PreModelling_Test$Class <- as.factor(PMA_PreModelling_Test$Class)
PMA_PreModelling_Test$E2 <- as.factor(PMA_PreModelling_Test$E2)
PMA_PreModelling_Test$E3 <- as.factor(PMA_PreModelling_Test$E3)
PMA_PreModelling_Test$E4 <- as.factor(PMA_PreModelling_Test$E4)
##################################
# Gathering descriptive statistics
##################################
(PMA_PreModelling_Test_Skimmed <- skim(PMA_PreModelling_Test))| Name | PMA_PreModelling_Test |
| Number of rows | 66 |
| Number of columns | 128 |
| _______________________ | |
| Column type frequency: | |
| factor | 4 |
| numeric | 124 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Class | 0 | 1 | FALSE | 2 | Con: 48, Imp: 18 |
| E4 | 0 | 1 | FALSE | 2 | 0: 46, 1: 20 |
| E3 | 0 | 1 | FALSE | 2 | 1: 65, 0: 1 |
| E2 | 0 | 1 | FALSE | 2 | 0: 62, 1: 4 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ACE_CD143_Angiotensin_Converti | 0 | 1 | 1.31 | 0.59 | -0.55 | 0.95 | 1.30 | 1.63 | 3.09 | ▁▃▇▃▁ |
| ACTH_Adrenocorticotropic_Hormon | 0 | 1 | -1.53 | 0.25 | -2.21 | -1.71 | -1.54 | -1.39 | -0.80 | ▁▃▇▃▁ |
| AXL | 0 | 1 | 0.25 | 0.45 | -0.74 | -0.08 | 0.28 | 0.61 | 1.29 | ▃▃▇▅▂ |
| Adiponectin | 0 | 1 | -5.30 | 0.66 | -7.06 | -5.74 | -5.36 | -4.92 | -3.47 | ▁▅▇▃▁ |
| Alpha_1_Antichymotrypsin | 0 | 1 | 1.31 | 0.38 | 0.18 | 1.06 | 1.31 | 1.57 | 2.22 | ▁▃▇▆▂ |
| Alpha_1_Antitrypsin | 0 | 1 | -13.49 | 1.76 | -18.17 | -14.70 | -13.59 | -12.31 | -10.06 | ▁▃▇▆▃ |
| Alpha_1_Microglobulin | 0 | 1 | -2.98 | 0.49 | -4.14 | -3.28 | -3.01 | -2.67 | -1.90 | ▂▅▇▅▂ |
| Alpha_2_Macroglobulin | 0 | 1 | -162.89 | 40.98 | -238.64 | -186.64 | -162.93 | -136.53 | -50.17 | ▃▇▇▃▁ |
| Angiopoietin_2_ANG_2 | 0 | 1 | 0.60 | 0.35 | -0.05 | 0.35 | 0.56 | 0.79 | 1.77 | ▃▇▅▁▁ |
| Angiotensinogen | 0 | 1 | 2.27 | 0.23 | 1.71 | 2.07 | 2.28 | 2.43 | 2.75 | ▁▇▇▇▂ |
| Apolipoprotein_A_IV | 0 | 1 | -1.87 | 0.38 | -2.75 | -2.19 | -1.90 | -1.53 | -1.11 | ▂▆▇▆▅ |
| Apolipoprotein_A1 | 0 | 1 | -7.49 | 0.42 | -8.57 | -7.82 | -7.50 | -7.18 | -6.65 | ▁▆▇▇▃ |
| Apolipoprotein_A2 | 0 | 1 | -0.69 | 0.48 | -1.97 | -0.94 | -0.70 | -0.35 | 0.53 | ▁▃▇▅▁ |
| Apolipoprotein_B | 0 | 1 | -5.65 | 1.34 | -8.19 | -6.75 | -5.82 | -4.60 | -2.34 | ▃▇▅▅▂ |
| Apolipoprotein_CI | 0 | 1 | -1.63 | 0.45 | -2.85 | -1.90 | -1.61 | -1.31 | -0.46 | ▂▅▇▅▁ |
| Apolipoprotein_CIII | 0 | 1 | -2.52 | 0.46 | -3.86 | -2.78 | -2.56 | -2.23 | -1.39 | ▁▂▇▃▂ |
| Apolipoprotein_D | 0 | 1 | 1.39 | 0.41 | 0.26 | 1.13 | 1.39 | 1.69 | 2.64 | ▁▇▇▅▁ |
| Apolipoprotein_E | 0 | 1 | 2.72 | 0.88 | 0.66 | 2.15 | 2.82 | 3.24 | 4.68 | ▂▅▇▅▂ |
| Apolipoprotein_H | 0 | 1 | -0.32 | 0.33 | -1.16 | -0.53 | -0.29 | -0.10 | 0.44 | ▂▅▇▆▂ |
| B_Lymphocyte_Chemoattractant_BL | 0 | 1 | 1.88 | 0.57 | 0.73 | 1.53 | 1.85 | 2.37 | 2.98 | ▂▆▇▆▃ |
| BMP_6 | 0 | 1 | -1.94 | 0.35 | -2.67 | -2.15 | -1.96 | -1.68 | -1.18 | ▃▃▇▆▂ |
| Beta_2_Microglobulin | 0 | 1 | 0.16 | 0.31 | -0.51 | -0.06 | 0.18 | 0.41 | 0.83 | ▂▇▇▆▂ |
| Betacellulin | 0 | 1 | 52.74 | 10.58 | 32.00 | 46.00 | 51.00 | 59.75 | 80.00 | ▂▇▅▃▁ |
| C_Reactive_Protein | 0 | 1 | -6.00 | 1.17 | -8.11 | -6.73 | -6.17 | -5.37 | -3.41 | ▃▇▇▃▃ |
| CD40 | 0 | 1 | -1.28 | 0.24 | -1.94 | -1.44 | -1.26 | -1.10 | -0.78 | ▂▃▅▇▂ |
| CD5L | 0 | 1 | -0.09 | 0.49 | -1.97 | -0.37 | -0.05 | 0.24 | 0.92 | ▁▁▃▇▂ |
| Calbindin | 0 | 1 | 21.49 | 4.49 | 10.81 | 18.88 | 21.06 | 24.00 | 35.36 | ▂▆▇▂▁ |
| Calcitonin | 0 | 1 | 1.73 | 0.89 | -0.71 | 1.20 | 1.68 | 2.26 | 4.11 | ▁▅▇▅▁ |
| CgA | 0 | 1 | 320.22 | 75.26 | 166.55 | 268.21 | 324.75 | 362.27 | 494.53 | ▃▃▇▃▂ |
| Clusterin_Apo_J | 0 | 1 | 2.85 | 0.35 | 1.93 | 2.56 | 2.83 | 3.04 | 3.76 | ▁▆▇▅▁ |
| Complement_3 | 0 | 1 | -15.91 | 2.60 | -22.40 | -17.50 | -15.90 | -14.34 | -10.23 | ▁▃▇▃▂ |
| Complement_Factor_H | 0 | 1 | 3.39 | 1.26 | 0.28 | 2.60 | 3.40 | 4.25 | 6.56 | ▁▅▇▅▁ |
| Connective_Tissue_Growth_Factor | 0 | 1 | 0.75 | 0.22 | 0.10 | 0.59 | 0.74 | 0.88 | 1.41 | ▁▃▇▃▁ |
| Cortisol | 0 | 1 | 10.46 | 3.85 | 0.10 | 8.90 | 10.00 | 12.00 | 22.00 | ▁▂▇▂▁ |
| Creatine_Kinase_MB | 0 | 1 | -1.65 | 0.10 | -1.87 | -1.72 | -1.65 | -1.59 | -1.43 | ▂▅▇▅▂ |
| Cystatin_C | 0 | 1 | 8.58 | 0.42 | 7.73 | 8.30 | 8.54 | 8.84 | 9.69 | ▃▇▇▃▂ |
| EGF_R | 0 | 1 | -0.70 | 0.26 | -1.27 | -0.89 | -0.69 | -0.50 | 0.19 | ▂▆▇▁▁ |
| EN_RAGE | 0 | 1 | -3.60 | 0.98 | -8.38 | -4.18 | -3.69 | -3.22 | -0.87 | ▁▁▅▇▁ |
| ENA_78 | 0 | 1 | -1.38 | 0.01 | -1.41 | -1.38 | -1.37 | -1.37 | -1.35 | ▂▂▆▇▂ |
| Eotaxin_3 | 0 | 1 | 55.55 | 18.33 | 23.00 | 43.00 | 54.00 | 64.00 | 107.00 | ▅▇▆▂▁ |
| FAS | 0 | 1 | -0.54 | 0.30 | -1.11 | -0.71 | -0.58 | -0.34 | 0.18 | ▃▇▇▅▂ |
| FSH_Follicle_Stimulation_Hormon | 0 | 1 | -1.06 | 0.34 | -1.81 | -1.27 | -0.98 | -0.81 | -0.48 | ▂▂▅▇▃ |
| Fas_Ligand | 0 | 1 | 2.65 | 1.02 | 0.29 | 2.07 | 2.67 | 3.16 | 5.38 | ▂▅▇▃▁ |
| Fatty_Acid_Binding_Protein | 0 | 1 | 1.29 | 0.79 | -0.46 | 0.80 | 1.19 | 1.92 | 3.22 | ▂▇▆▆▂ |
| Ferritin | 0 | 1 | 2.71 | 0.87 | 0.90 | 2.15 | 2.63 | 3.17 | 4.93 | ▂▆▇▂▂ |
| Fetuin_A | 0 | 1 | 1.31 | 0.41 | 0.53 | 1.03 | 1.31 | 1.61 | 2.21 | ▅▇▇▇▂ |
| Fibrinogen | 0 | 1 | -7.36 | 0.63 | -9.37 | -7.80 | -7.32 | -6.97 | -6.17 | ▁▂▇▇▅ |
| GRO_alpha | 0 | 1 | 1.38 | 0.04 | 1.27 | 1.35 | 1.37 | 1.40 | 1.51 | ▁▇▇▂▁ |
| Gamma_Interferon_induced_Monokin | 0 | 1 | 2.77 | 0.12 | 2.54 | 2.70 | 2.77 | 2.83 | 3.05 | ▂▅▇▂▂ |
| Glutathione_S_Transferase_alpha | 0 | 1 | 0.94 | 0.17 | 0.57 | 0.83 | 0.95 | 1.05 | 1.31 | ▃▅▇▇▂ |
| HB_EGF | 0 | 1 | 6.84 | 1.43 | 3.52 | 5.95 | 6.98 | 7.75 | 10.36 | ▂▆▇▇▂ |
| HCC_4 | 0 | 1 | -3.54 | 0.35 | -4.34 | -3.77 | -3.54 | -3.35 | -2.49 | ▂▇▇▃▁ |
| Hepatocyte_Growth_Factor_HGF | 0 | 1 | 0.18 | 0.34 | -0.62 | -0.06 | 0.18 | 0.34 | 1.10 | ▁▆▇▃▁ |
| I_309 | 0 | 1 | 2.92 | 0.36 | 2.04 | 2.72 | 2.94 | 3.14 | 3.69 | ▂▂▇▃▂ |
| ICAM_1 | 0 | 1 | -0.60 | 0.35 | -1.47 | -0.77 | -0.59 | -0.36 | 0.36 | ▂▂▇▅▁ |
| IGF_BP_2 | 0 | 1 | 5.26 | 0.22 | 4.72 | 5.13 | 5.25 | 5.40 | 5.92 | ▁▅▇▂▁ |
| IL_11 | 0 | 1 | 4.65 | 1.33 | 2.03 | 3.96 | 4.84 | 5.48 | 8.69 | ▃▅▇▂▁ |
| IL_13 | 0 | 1 | 1.28 | 0.01 | 1.23 | 1.27 | 1.28 | 1.29 | 1.31 | ▁▁▇▇▃ |
| IL_16 | 0 | 1 | 2.82 | 0.70 | 0.96 | 2.44 | 2.88 | 3.35 | 4.10 | ▂▂▇▇▅ |
| IL_17E | 0 | 1 | 4.77 | 1.33 | 1.58 | 3.64 | 4.72 | 5.41 | 8.08 | ▁▇▇▃▂ |
| IL_1alpha | 0 | 1 | -7.55 | 0.44 | -8.47 | -7.85 | -7.56 | -7.28 | -6.38 | ▂▇▇▃▁ |
| IL_3 | 0 | 1 | -3.98 | 0.52 | -5.52 | -4.32 | -3.96 | -3.58 | -3.08 | ▁▃▇▇▆ |
| IL_4 | 0 | 1 | 1.74 | 0.52 | 0.53 | 1.46 | 1.72 | 2.07 | 2.71 | ▁▅▇▃▅ |
| IL_5 | 0 | 1 | 0.23 | 0.44 | -1.05 | -0.03 | 0.22 | 0.53 | 1.13 | ▁▂▇▇▂ |
| IL_6 | 0 | 1 | -0.05 | 0.52 | -1.53 | -0.41 | -0.07 | 0.35 | 1.01 | ▁▂▇▇▂ |
| IL_6_Receptor | 0 | 1 | 0.06 | 0.35 | -0.75 | -0.20 | 0.00 | 0.27 | 0.77 | ▂▇▇▆▅ |
| IL_7 | 0 | 1 | 3.14 | 0.85 | 1.31 | 2.38 | 3.15 | 3.71 | 5.00 | ▁▇▅▆▂ |
| IL_8 | 0 | 1 | 1.70 | 0.04 | 1.62 | 1.68 | 1.70 | 1.73 | 1.84 | ▂▇▆▂▁ |
| IP_10_Inducible_Protein_10 | 0 | 1 | 5.64 | 0.51 | 4.26 | 5.32 | 5.62 | 5.92 | 7.21 | ▁▅▇▃▁ |
| IgA | 0 | 1 | -6.07 | 0.64 | -7.62 | -6.57 | -6.01 | -5.61 | -4.73 | ▁▆▇▇▂ |
| Insulin | 0 | 1 | -1.20 | 0.32 | -2.01 | -1.45 | -1.22 | -1.01 | -0.50 | ▂▆▇▇▃ |
| Kidney_Injury_Molecule_1_KIM_1 | 0 | 1 | -1.19 | 0.03 | -1.25 | -1.21 | -1.19 | -1.17 | -1.12 | ▃▇▇▇▂ |
| LOX_1 | 0 | 1 | 1.21 | 0.45 | 0.00 | 0.96 | 1.22 | 1.44 | 2.40 | ▁▃▇▃▁ |
| Leptin | 0 | 1 | -1.44 | 0.25 | -1.95 | -1.63 | -1.43 | -1.24 | -0.84 | ▃▇▆▇▂ |
| Lipoprotein_a | 0 | 1 | -4.51 | 0.91 | -6.57 | -5.12 | -4.66 | -4.02 | -2.04 | ▂▇▇▅▁ |
| MCP_1 | 0 | 1 | 6.48 | 0.25 | 5.89 | 6.32 | 6.48 | 6.63 | 7.06 | ▁▅▇▅▂ |
| MCP_2 | 0 | 1 | 1.81 | 0.60 | 0.40 | 1.53 | 1.85 | 2.08 | 3.75 | ▂▅▇▁▁ |
| MIF | 0 | 1 | -1.93 | 0.31 | -2.80 | -2.12 | -1.97 | -1.71 | -1.11 | ▁▃▇▃▁ |
| MIP_1alpha | 0 | 1 | 3.90 | 1.04 | 1.01 | 3.30 | 3.74 | 4.69 | 5.74 | ▁▂▇▆▅ |
| MIP_1beta | 0 | 1 | 2.78 | 0.39 | 1.92 | 2.48 | 2.77 | 3.08 | 3.78 | ▃▆▇▆▁ |
| MMP_2 | 0 | 1 | 3.03 | 0.95 | 0.62 | 2.55 | 2.99 | 3.48 | 6.10 | ▁▃▇▁▁ |
| MMP_3 | 0 | 1 | -2.49 | 0.59 | -3.65 | -2.85 | -2.53 | -2.12 | -1.17 | ▃▆▇▅▂ |
| MMP10 | 0 | 1 | -3.68 | 0.44 | -4.95 | -4.07 | -3.61 | -3.33 | -2.90 | ▁▃▆▇▅ |
| MMP7 | 0 | 1 | -4.01 | 1.55 | -7.53 | -4.96 | -4.03 | -3.16 | -0.20 | ▂▃▇▂▁ |
| Myoglobin | 0 | 1 | -1.42 | 0.91 | -3.30 | -2.02 | -1.59 | -0.78 | 1.13 | ▂▇▆▃▁ |
| NT_proBNP | 0 | 1 | 4.49 | 0.39 | 3.61 | 4.17 | 4.48 | 4.79 | 5.40 | ▂▇▇▆▂ |
| NrCAM | 0 | 1 | 4.29 | 0.58 | 2.89 | 3.87 | 4.32 | 4.72 | 5.69 | ▂▆▇▆▁ |
| Osteopontin | 0 | 1 | 5.18 | 0.40 | 4.08 | 4.89 | 5.17 | 5.41 | 6.32 | ▁▆▇▃▁ |
| PAI_1 | 0 | 1 | 0.00 | 0.41 | -0.99 | -0.33 | 0.00 | 0.30 | 0.89 | ▂▆▇▇▂ |
| PAPP_A | 0 | 1 | -2.84 | 0.14 | -3.15 | -2.97 | -2.84 | -2.72 | -2.49 | ▂▇▇▆▁ |
| PLGF | 0 | 1 | 3.88 | 0.38 | 2.64 | 3.69 | 3.89 | 4.12 | 4.71 | ▁▂▇▇▂ |
| PYY | 0 | 1 | 2.98 | 0.29 | 2.40 | 2.83 | 3.00 | 3.18 | 3.74 | ▃▇▇▅▁ |
| Pancreatic_polypeptide | 0 | 1 | -0.01 | 0.70 | -1.61 | -0.51 | 0.14 | 0.47 | 1.50 | ▂▅▅▇▁ |
| Prolactin | 0 | 1 | 0.05 | 0.29 | -0.39 | -0.17 | 0.00 | 0.18 | 0.79 | ▇▇▆▃▂ |
| Prostatic_Acid_Phosphatase | 0 | 1 | -1.69 | 0.05 | -1.80 | -1.74 | -1.69 | -1.66 | -1.54 | ▂▇▇▂▁ |
| Protein_S | 0 | 1 | -2.27 | 0.41 | -3.15 | -2.58 | -2.26 | -1.92 | -1.55 | ▃▅▇▇▆ |
| Pulmonary_and_Activation_Regulat | 0 | 1 | -1.50 | 0.45 | -2.44 | -1.83 | -1.51 | -1.17 | -0.45 | ▃▇▆▇▂ |
| RANTES | 0 | 1 | -6.54 | 0.29 | -7.24 | -6.73 | -6.57 | -6.39 | -5.84 | ▁▆▇▃▂ |
| Resistin | 0 | 1 | -18.24 | 5.18 | -30.16 | -22.13 | -18.01 | -15.20 | -6.59 | ▂▅▇▅▂ |
| S100b | 0 | 1 | 1.18 | 0.37 | 0.19 | 0.96 | 1.16 | 1.38 | 2.20 | ▁▃▇▂▁ |
| SGOT | 0 | 1 | -0.49 | 0.37 | -1.90 | -0.75 | -0.48 | -0.21 | 0.18 | ▁▁▆▇▅ |
| SHBG | 0 | 1 | -2.69 | 0.49 | -3.73 | -3.05 | -2.71 | -2.34 | -1.56 | ▂▇▇▆▂ |
| SOD | 0 | 1 | 5.30 | 0.41 | 4.38 | 5.01 | 5.31 | 5.55 | 6.46 | ▂▇▇▃▁ |
| Serum_Amyloid_P | 0 | 1 | -6.08 | 0.52 | -7.18 | -6.44 | -6.21 | -5.61 | -4.70 | ▂▇▅▃▁ |
| Sortilin | 0 | 1 | 3.79 | 0.87 | 1.51 | 3.18 | 3.87 | 4.37 | 5.68 | ▂▅▇▆▃ |
| Stem_Cell_Factor | 0 | 1 | 3.27 | 0.35 | 2.22 | 3.04 | 3.31 | 3.47 | 4.08 | ▁▅▇▇▂ |
| TGF_alpha | 0 | 1 | 9.78 | 1.11 | 7.50 | 9.06 | 9.60 | 10.61 | 13.08 | ▃▇▆▃▁ |
| TIMP_1 | 0 | 1 | 11.52 | 1.64 | 8.20 | 10.53 | 11.34 | 12.35 | 16.55 | ▅▇▇▃▁ |
| TNF_RII | 0 | 1 | -0.63 | 0.36 | -1.66 | -0.87 | -0.65 | -0.33 | 0.41 | ▁▅▇▅▁ |
| TRAIL_R3 | 0 | 1 | -0.59 | 0.23 | -1.31 | -0.73 | -0.56 | -0.47 | 0.10 | ▁▃▇▂▁ |
| TTR_prealbumin | 0 | 1 | 2.85 | 0.14 | 2.48 | 2.77 | 2.89 | 2.94 | 3.09 | ▁▃▅▇▅ |
| Tamm_Horsfall_Protein_THP | 0 | 1 | -3.12 | 0.03 | -3.21 | -3.14 | -3.13 | -3.10 | -3.04 | ▁▅▇▃▂ |
| Thrombomodulin | 0 | 1 | -1.53 | 0.25 | -2.05 | -1.68 | -1.53 | -1.34 | -1.02 | ▂▆▇▆▂ |
| Thrombopoietin | 0 | 1 | -0.72 | 0.21 | -1.54 | -0.84 | -0.70 | -0.63 | -0.30 | ▁▁▆▇▃ |
| Thymus_Expressed_Chemokine_TECK | 0 | 1 | 3.77 | 0.73 | 2.14 | 3.28 | 3.75 | 4.32 | 5.68 | ▃▆▇▅▂ |
| Thyroid_Stimulating_Hormone | 0 | 1 | -4.22 | 0.79 | -6.19 | -4.73 | -4.27 | -3.83 | -2.04 | ▁▅▇▃▁ |
| Thyroxine_Binding_Globulin | 0 | 1 | -1.49 | 0.40 | -2.30 | -1.71 | -1.49 | -1.24 | -0.60 | ▃▅▇▆▂ |
| Tissue_Factor | 0 | 1 | 1.14 | 0.54 | 0.00 | 0.71 | 1.15 | 1.51 | 2.71 | ▂▆▇▂▁ |
| Transferrin | 0 | 1 | 2.90 | 0.29 | 2.28 | 2.71 | 2.89 | 3.14 | 3.50 | ▃▃▇▅▃ |
| Trefoil_Factor_3_TFF3 | 0 | 1 | -3.95 | 0.34 | -4.91 | -4.20 | -3.91 | -3.77 | -3.17 | ▁▂▇▆▂ |
| VCAM_1 | 0 | 1 | 2.64 | 0.32 | 2.03 | 2.42 | 2.67 | 2.83 | 3.47 | ▅▃▇▃▁ |
| VEGF | 0 | 1 | 16.70 | 2.10 | 12.23 | 15.03 | 17.08 | 18.19 | 21.18 | ▃▇▇▆▂ |
| Vitronectin | 0 | 1 | -0.27 | 0.32 | -1.08 | -0.46 | -0.28 | -0.05 | 0.41 | ▁▅▇▆▂ |
| von_Willebrand_Factor | 0 | 1 | -4.01 | 0.38 | -4.92 | -4.27 | -4.02 | -3.73 | -3.06 | ▂▇▇▆▂ |
###################################
# Verifying the data dimensions
# for the test set
###################################
dim(PMA_PreModelling_Test)## [1] 66 1281.4 Data Exploration
Exploratory data analysis:
[A] Numeric variables which demonstrated differential relationships with the Class response variable between its Control and Impaired levels include:
[A.1] Fibrinogen variable (numeric)
[A.2] GRO_alpha variable (numeric)
[A.3] FAS variable (numeric)
[A.4] Eotaxin_3 variable (numeric)
[A.5] Creatine_Kinase_MB variable (numeric)
[A.6] IGF_BP2 variable (numeric)
[A.7] Gamma_Interferon_Induced_Monokin variable (numeric)
[A.8] MCP_2 variable (numeric)
[A.9] MIF variable (numeric)
[A.10] Pancreatic_polypteptide variable (numeric)
[A.11] PAI_1 variable (numeric)
[A.12] NT_proBNP variable (numeric)
[A.13] MMP10 variable (numeric)
[A.14] MMP7 variable (numeric)
[A.15] TRAIL_R3 variable (numeric)
[A.16] Pulmonary_and_Activation_Regulat variable (numeric)
[A.17] Resistin variable (numeric)
[A.18] VEGF variable (numeric)
[A.19] Thrombopoietin variable (numeric)
[A.20] Thymus_Expressed_Chemokine_TECK variable (numeric)
[B] Factor variables which demonstrated relatively better differentiation of the Class response variable between its Control and Impaired levels include:
[B.1] E2 variable (factor)
[B.2] E4 variable (factor)
##################################
# Loading dataset
##################################
EDA <- PMA_PreModelling_Train
##################################
# Listing all predictors
##################################
EDA.Predictors <- EDA[,!names(EDA) %in% c("Class")]
##################################
# Listing all numeric predictors
##################################
EDA.Predictors.Numeric <- EDA.Predictors[,sapply(EDA.Predictors, is.numeric)]
ncol(EDA.Predictors.Numeric)## [1] 124names(EDA.Predictors.Numeric)## [1] "ACE_CD143_Angiotensin_Converti" "ACTH_Adrenocorticotropic_Hormon"
## [3] "AXL" "Adiponectin"
## [5] "Alpha_1_Antichymotrypsin" "Alpha_1_Antitrypsin"
## [7] "Alpha_1_Microglobulin" "Alpha_2_Macroglobulin"
## [9] "Angiopoietin_2_ANG_2" "Angiotensinogen"
## [11] "Apolipoprotein_A_IV" "Apolipoprotein_A1"
## [13] "Apolipoprotein_A2" "Apolipoprotein_B"
## [15] "Apolipoprotein_CI" "Apolipoprotein_CIII"
## [17] "Apolipoprotein_D" "Apolipoprotein_E"
## [19] "Apolipoprotein_H" "B_Lymphocyte_Chemoattractant_BL"
## [21] "BMP_6" "Beta_2_Microglobulin"
## [23] "Betacellulin" "C_Reactive_Protein"
## [25] "CD40" "CD5L"
## [27] "Calbindin" "Calcitonin"
## [29] "CgA" "Clusterin_Apo_J"
## [31] "Complement_3" "Complement_Factor_H"
## [33] "Connective_Tissue_Growth_Factor" "Cortisol"
## [35] "Creatine_Kinase_MB" "Cystatin_C"
## [37] "EGF_R" "EN_RAGE"
## [39] "ENA_78" "Eotaxin_3"
## [41] "FAS" "FSH_Follicle_Stimulation_Hormon"
## [43] "Fas_Ligand" "Fatty_Acid_Binding_Protein"
## [45] "Ferritin" "Fetuin_A"
## [47] "Fibrinogen" "GRO_alpha"
## [49] "Gamma_Interferon_induced_Monokin" "Glutathione_S_Transferase_alpha"
## [51] "HB_EGF" "HCC_4"
## [53] "Hepatocyte_Growth_Factor_HGF" "I_309"
## [55] "ICAM_1" "IGF_BP_2"
## [57] "IL_11" "IL_13"
## [59] "IL_16" "IL_17E"
## [61] "IL_1alpha" "IL_3"
## [63] "IL_4" "IL_5"
## [65] "IL_6" "IL_6_Receptor"
## [67] "IL_7" "IL_8"
## [69] "IP_10_Inducible_Protein_10" "IgA"
## [71] "Insulin" "Kidney_Injury_Molecule_1_KIM_1"
## [73] "LOX_1" "Leptin"
## [75] "Lipoprotein_a" "MCP_1"
## [77] "MCP_2" "MIF"
## [79] "MIP_1alpha" "MIP_1beta"
## [81] "MMP_2" "MMP_3"
## [83] "MMP10" "MMP7"
## [85] "Myoglobin" "NT_proBNP"
## [87] "NrCAM" "Osteopontin"
## [89] "PAI_1" "PAPP_A"
## [91] "PLGF" "PYY"
## [93] "Pancreatic_polypeptide" "Prolactin"
## [95] "Prostatic_Acid_Phosphatase" "Protein_S"
## [97] "Pulmonary_and_Activation_Regulat" "RANTES"
## [99] "Resistin" "S100b"
## [101] "SGOT" "SHBG"
## [103] "SOD" "Serum_Amyloid_P"
## [105] "Sortilin" "Stem_Cell_Factor"
## [107] "TGF_alpha" "TIMP_1"
## [109] "TNF_RII" "TRAIL_R3"
## [111] "TTR_prealbumin" "Tamm_Horsfall_Protein_THP"
## [113] "Thrombomodulin" "Thrombopoietin"
## [115] "Thymus_Expressed_Chemokine_TECK" "Thyroid_Stimulating_Hormone"
## [117] "Thyroxine_Binding_Globulin" "Tissue_Factor"
## [119] "Transferrin" "Trefoil_Factor_3_TFF3"
## [121] "VCAM_1" "VEGF"
## [123] "Vitronectin" "von_Willebrand_Factor"##################################
# Listing all factor predictors
##################################
EDA.Predictors.Factor <- EDA.Predictors[,sapply(EDA.Predictors, is.factor)]
ncol(EDA.Predictors.Factor)## [1] 3names(EDA.Predictors.Factor)## [1] "E4" "E3" "E2"##################################
# Formulating the box plots
##################################
featurePlot(x = EDA.Predictors.Numeric[1:124],
y = EDA$Class,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
pch = "|",
layout=(c(4,4)))
##################################
# Restructuring the dataset for
# for barchart analysis
##################################
EDA.Bar.Source <- as.data.frame(cbind(EDA$Class,
EDA.Predictors.Factor))
names(EDA.Bar.Source) <- c("Class",names(EDA.Predictors.Factor))
ncol(EDA.Bar.Source)## [1] 4##################################
# Creating a function to formulate
# the proportions table
##################################
EDA.PropTable.Function <- function(FactorVar) {
EDA.Bar.Source.FactorVar <- EDA.Bar.Source[,c("Class",
FactorVar)]
EDA.Bar.Source.FactorVar.Prop <- as.data.frame(prop.table(table(EDA.Bar.Source.FactorVar), 2))
names(EDA.Bar.Source.FactorVar.Prop)[2] <- "Class"
EDA.Bar.Source.FactorVar.Prop$Variable <- rep(FactorVar,nrow(EDA.Bar.Source.FactorVar.Prop))
return(EDA.Bar.Source.FactorVar.Prop)
}
EDA.Bar.Source.FactorVar.Prop <- rbind(EDA.PropTable.Function("E2"),
EDA.PropTable.Function("E3"),
EDA.PropTable.Function("E4"))
(EDA.Barchart.FactorVar <- barchart(EDA.Bar.Source.FactorVar.Prop[,3] ~
EDA.Bar.Source.FactorVar.Prop[,2] | EDA.Bar.Source.FactorVar.Prop[,4],
data=EDA.Bar.Source.FactorVar.Prop,
groups = EDA.Bar.Source.FactorVar.Prop[,1],
stack=TRUE,
ylab = "Proportion",
xlab = "Class",
auto.key = list(adj = 1),
layout=(c(3,1))))
1.5 Recursive Feature Elimination (RFE)
1.5.1 Random Forest Without RFE (RF_FULL)
Random Forest is an ensemble learning method made up of a large set of small decision trees called estimators, with each producing its own prediction. The random forest model aggregates the predictions of the estimators to produce a more accurate prediction. The algorithm involves bootstrap aggregating (where smaller subsets of the training data are repeatedly subsampled with replacement), random subspacing (where a subset of features are sampled and used to train each individual estimator), estimator training (where unpruned decision trees are formulated for each estimator) and inference by aggregating the predictions of all estimators.
[A] The random forest model from the randomForest package was implemented without recursive feature elimination through the caret package.
[B] The model contains 1 hyperparameter:
[B.1] mtry = number of randomly selected predictors held constant at a value of 11
[C] The cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves mtry=11
[C.2] ROC Curve AUC = 0.78268
[D] The model allows for ranking of predictors in terms of variable importance. The top-performing predictors in the model are as follows:
[D.1] MMP10 variable (numeric)
[D.2] Crystatin_C variable (numeric)
[D.3] MMP7 variable (numeric)
[D.4] TRAIL_R3 variable (numeric)
[D.5] Pancreatic_polypeptide variable (numeric)
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.79803
##################################
# Converting all predictors to numeric
# for both train and test data
##################################
for (i in 1:ncol(PMA_PreModelling_Train)){
if (names(PMA_PreModelling_Train)[i]!="Class"){
PMA_PreModelling_Train[,i] <- as.numeric(PMA_PreModelling_Train[,i])
}
}
summary(PMA_PreModelling_Train)## ACE_CD143_Angiotensin_Converti ACTH_Adrenocorticotropic_Hormon
## Min. :-0.6756 Min. :-2.207
## 1st Qu.: 0.9462 1st Qu.:-1.715
## Median : 1.3013 Median :-1.561
## Mean : 1.3198 Mean :-1.538
## 3rd Qu.: 1.7191 3rd Qu.:-1.347
## Max. : 2.8398 Max. :-0.844
## AXL Adiponectin Alpha_1_Antichymotrypsin
## Min. :-0.9230 Min. :-6.725 Min. :0.2624
## 1st Qu.: 0.0000 1st Qu.:-5.669 1st Qu.:1.1314
## Median : 0.2804 Median :-5.185 Median :1.3610
## Mean : 0.3093 Mean :-5.201 Mean :1.3605
## 3rd Qu.: 0.6077 3rd Qu.:-4.780 3rd Qu.:1.5892
## Max. : 1.5214 Max. :-3.507 Max. :2.3026
## Alpha_1_Antitrypsin Alpha_1_Microglobulin Alpha_2_Macroglobulin
## Min. :-17.028 Min. :-4.343 Min. :-289.68
## 1st Qu.:-14.071 1st Qu.:-3.270 1st Qu.:-186.64
## Median :-13.004 Median :-2.937 Median :-160.01
## Mean :-13.052 Mean :-2.932 Mean :-158.61
## 3rd Qu.:-12.096 3rd Qu.:-2.590 3rd Qu.:-134.62
## Max. : -8.192 Max. :-1.772 Max. : -59.46
## Angiopoietin_2_ANG_2 Angiotensinogen Apolipoprotein_A_IV Apolipoprotein_A1
## Min. :-0.5447 Min. :1.752 Min. :-2.9565 Min. :-8.680
## 1st Qu.: 0.4700 1st Qu.:2.119 1st Qu.:-2.1203 1st Qu.:-7.763
## Median : 0.6419 Median :2.320 Median :-1.8326 Median :-7.470
## Mean : 0.6730 Mean :2.318 Mean :-1.8544 Mean :-7.483
## 3rd Qu.: 0.8755 3rd Qu.:2.497 3rd Qu.:-1.6094 3rd Qu.:-7.209
## Max. : 1.5261 Max. :2.881 Max. :-0.7765 Max. :-6.166
## Apolipoprotein_A2 Apolipoprotein_B Apolipoprotein_CI Apolipoprotein_CIII
## Min. :-1.8971 Min. :-9.937 Min. :-3.3242 Min. :-3.689
## 1st Qu.:-0.9676 1st Qu.:-6.630 1st Qu.:-1.8326 1st Qu.:-2.773
## Median :-0.6733 Median :-5.703 Median :-1.6094 Median :-2.526
## Mean :-0.6354 Mean :-5.578 Mean :-1.5833 Mean :-2.494
## 3rd Qu.:-0.3147 3rd Qu.:-4.539 3rd Qu.:-1.3667 3rd Qu.:-2.207
## Max. : 0.9555 Max. :-2.153 Max. :-0.2744 Max. :-1.238
## Apolipoprotein_D Apolipoprotein_E Apolipoprotein_H
## Min. :0.470 Min. :0.5911 Min. :-2.23379
## 1st Qu.:1.209 1st Qu.:2.3344 1st Qu.:-0.59782
## Median :1.411 Median :2.8181 Median :-0.37005
## Mean :1.440 Mean :2.8062 Mean :-0.32122
## 3rd Qu.:1.668 3rd Qu.:3.2863 3rd Qu.:-0.06112
## Max. :2.272 Max. :5.4442 Max. : 0.92696
## B_Lymphocyte_Chemoattractant_BL BMP_6 Beta_2_Microglobulin
## Min. :0.7318 Min. :-2.7612 Min. :-0.54473
## 1st Qu.:1.6731 1st Qu.:-2.1516 1st Qu.:-0.04082
## Median :1.9805 Median :-1.8774 Median : 0.18232
## Mean :2.0175 Mean :-1.9114 Mean : 0.16757
## 3rd Qu.:2.3714 3rd Qu.:-1.6753 3rd Qu.: 0.33647
## Max. :4.0237 Max. :-0.8166 Max. : 0.99325
## Betacellulin C_Reactive_Protein CD40 CD5L
## Min. :10.00 Min. :-8.517 Min. :-1.8644 Min. :-1.23787
## 1st Qu.:42.00 1st Qu.:-6.645 1st Qu.:-1.3761 1st Qu.:-0.35667
## Median :51.00 Median :-5.843 Median :-1.2734 Median :-0.06188
## Mean :51.01 Mean :-5.874 Mean :-1.2584 Mean :-0.05310
## 3rd Qu.:59.00 3rd Qu.:-5.083 3rd Qu.:-1.1238 3rd Qu.: 0.26236
## Max. :82.00 Max. :-2.937 Max. :-0.5475 Max. : 1.16315
## Calbindin Calcitonin CgA Clusterin_Apo_J
## Min. :10.96 Min. :-0.7134 Min. :135.6 Min. :1.872
## 1st Qu.:19.77 1st Qu.: 0.9555 1st Qu.:278.0 1st Qu.:2.708
## Median :22.25 Median : 1.6487 Median :331.5 Median :2.890
## Mean :22.43 Mean : 1.6788 Mean :333.3 Mean :2.882
## 3rd Qu.:24.80 3rd Qu.: 2.2824 3rd Qu.:392.1 3rd Qu.:3.045
## Max. :33.78 Max. : 3.8918 Max. :535.4 Max. :3.584
## Complement_3 Complement_Factor_H Connective_Tissue_Growth_Factor
## Min. :-23.387 Min. :-0.8387 Min. :0.1823
## 1st Qu.:-17.567 1st Qu.: 2.7531 1st Qu.:0.6419
## Median :-15.524 Median : 3.6000 Median :0.7885
## Mean :-15.610 Mean : 3.5541 Mean :0.7739
## 3rd Qu.:-13.882 3rd Qu.: 4.2548 3rd Qu.:0.9163
## Max. : -9.563 Max. : 7.6238 Max. :1.4110
## Cortisol Creatine_Kinase_MB Cystatin_C EGF_R
## Min. : 0.10 Min. :-1.872 Min. :7.432 Min. :-1.36135
## 1st Qu.: 9.80 1st Qu.:-1.724 1st Qu.:8.321 1st Qu.:-0.85727
## Median :12.00 Median :-1.671 Median :8.564 Median :-0.68354
## Mean :11.98 Mean :-1.674 Mean :8.586 Mean :-0.70130
## 3rd Qu.:14.00 3rd Qu.:-1.626 3rd Qu.:8.839 3rd Qu.:-0.54612
## Max. :29.00 Max. :-1.384 Max. :9.694 Max. :-0.06112
## EN_RAGE ENA_78 Eotaxin_3 FAS
## Min. :-8.3774 Min. :-1.405 Min. : 7.00 Min. :-1.5141
## 1st Qu.:-4.1997 1st Qu.:-1.381 1st Qu.: 44.00 1st Qu.:-0.7133
## Median :-3.6497 Median :-1.374 Median : 59.00 Median :-0.5276
## Mean :-3.6353 Mean :-1.372 Mean : 58.17 Mean :-0.5291
## 3rd Qu.:-3.1466 3rd Qu.:-1.364 3rd Qu.: 70.00 3rd Qu.:-0.3147
## Max. :-0.3857 Max. :-1.339 Max. :107.00 Max. : 0.3365
## FSH_Follicle_Stimulation_Hormon Fas_Ligand Fatty_Acid_Binding_Protein
## Min. :-2.11511 Min. :-0.1536 Min. :-1.0441
## 1st Qu.:-1.46606 1st Qu.: 2.3415 1st Qu.: 0.7998
## Median :-1.13570 Median : 3.1015 Median : 1.3865
## Mean :-1.14259 Mean : 2.9680 Mean : 1.3529
## 3rd Qu.:-0.87620 3rd Qu.: 3.6950 3rd Qu.: 1.8847
## Max. : 0.09715 Max. : 7.6328 Max. : 3.7055
## Ferritin Fetuin_A Fibrinogen GRO_alpha
## Min. :0.6077 Min. :0.470 Min. :-8.874 Min. :1.271
## 1st Qu.:2.2895 1st Qu.:1.099 1st Qu.:-7.717 1st Qu.:1.351
## Median :2.7749 Median :1.308 Median :-7.323 Median :1.382
## Mean :2.7646 Mean :1.350 Mean :-7.356 Mean :1.378
## 3rd Qu.:3.2915 3rd Qu.:1.609 3rd Qu.:-7.013 3rd Qu.:1.406
## Max. :4.6333 Max. :2.251 Max. :-5.843 Max. :1.495
## Gamma_Interferon_induced_Monokin Glutathione_S_Transferase_alpha
## Min. :2.393 Min. :0.5238
## 1st Qu.:2.707 1st Qu.:0.8439
## Median :2.783 Median :0.9677
## Mean :2.786 Mean :0.9512
## 3rd Qu.:2.873 3rd Qu.:1.0344
## Max. :3.065 Max. :1.3176
## HB_EGF HCC_4 Hepatocyte_Growth_Factor_HGF I_309
## Min. : 2.103 Min. :-4.510 Min. :-0.6349 Min. :1.758
## 1st Qu.: 5.786 1st Qu.:-3.730 1st Qu.: 0.0000 1st Qu.:2.708
## Median : 6.703 Median :-3.507 Median : 0.1823 Median :2.944
## Mean : 6.833 Mean :-3.500 Mean : 0.1963 Mean :2.958
## 3rd Qu.: 7.865 3rd Qu.:-3.270 3rd Qu.: 0.4055 3rd Qu.:3.219
## Max. :10.695 Max. :-2.120 Max. : 0.8755 Max. :4.143
## ICAM_1 IGF_BP_2 IL_11 IL_13
## Min. :-1.5332 Min. :4.635 Min. :1.755 Min. :1.259
## 1st Qu.:-0.8298 1st Qu.:5.179 1st Qu.:3.706 1st Qu.:1.274
## Median :-0.5903 Median :5.323 Median :4.805 Median :1.283
## Mean :-0.5908 Mean :5.317 Mean :4.725 Mean :1.284
## 3rd Qu.:-0.3828 3rd Qu.:5.453 3rd Qu.:5.776 3rd Qu.:1.290
## Max. : 0.5171 Max. :5.948 Max. :8.491 Max. :1.321
## IL_16 IL_17E IL_1alpha IL_3
## Min. :1.187 Min. :1.052 Min. :-8.517 Min. :-5.915
## 1st Qu.:2.521 1st Qu.:4.149 1st Qu.:-7.824 1st Qu.:-4.269
## Median :2.909 Median :4.749 Median :-7.524 Median :-3.912
## Mean :2.929 Mean :4.855 Mean :-7.514 Mean :-3.941
## 3rd Qu.:3.351 3rd Qu.:5.631 3rd Qu.:-7.264 3rd Qu.:-3.631
## Max. :4.937 Max. :8.952 Max. :-5.952 Max. :-2.453
## IL_4 IL_5 IL_6 IL_6_Receptor
## Min. :0.5306 Min. :-1.4271 Min. :-1.5343 Min. :-0.67562
## 1st Qu.:1.4586 1st Qu.:-0.1221 1st Qu.:-0.4127 1st Qu.:-0.12541
## Median :1.8083 Median : 0.1823 Median :-0.1599 Median : 0.09669
## Mean :1.7732 Mean : 0.1866 Mean :-0.1540 Mean : 0.09492
## 3rd Qu.:2.1459 3rd Qu.: 0.4700 3rd Qu.: 0.1410 3rd Qu.: 0.35404
## Max. :3.0445 Max. : 1.9459 Max. : 1.8138 Max. : 0.83099
## IL_7 IL_8 IP_10_Inducible_Protein_10 IgA
## Min. :0.5598 Min. :1.574 Min. :4.317 Min. :-10.520
## 1st Qu.:2.1548 1st Qu.:1.680 1st Qu.:5.398 1st Qu.: -6.645
## Median :2.7934 Median :1.705 Median :5.753 Median : -6.119
## Mean :2.8392 Mean :1.704 Mean :5.755 Mean : -6.121
## 3rd Qu.:3.7055 3rd Qu.:1.728 3rd Qu.:6.064 3rd Qu.: -5.573
## Max. :5.7056 Max. :1.807 Max. :7.501 Max. : -4.200
## Insulin Kidney_Injury_Molecule_1_KIM_1 LOX_1
## Min. :-2.1692 Min. :-1.256 Min. :0.000
## 1st Qu.:-1.4466 1st Qu.:-1.204 1st Qu.:1.030
## Median :-1.2462 Median :-1.183 Median :1.281
## Mean :-1.2329 Mean :-1.185 Mean :1.283
## 3rd Qu.:-1.0340 3rd Qu.:-1.164 3rd Qu.:1.526
## Max. :-0.1586 Max. :-1.105 Max. :2.272
## Leptin Lipoprotein_a MCP_1 MCP_2
## Min. :-2.1468 Min. :-6.812 Min. :5.826 Min. :0.4006
## 1st Qu.:-1.6996 1st Qu.:-5.308 1st Qu.:6.319 1st Qu.:1.5304
## Median :-1.5047 Median :-4.605 Median :6.494 Median :1.8528
## Mean :-1.5042 Mean :-4.417 Mean :6.497 Mean :1.8691
## 3rd Qu.:-1.3295 3rd Qu.:-3.490 3rd Qu.:6.678 3rd Qu.:2.1821
## Max. :-0.6206 Max. :-1.386 Max. :7.230 Max. :4.0237
## MIF MIP_1alpha MIP_1beta MMP_2
## Min. :-2.847 Min. :0.9346 Min. :1.946 Min. :0.09809
## 1st Qu.:-2.120 1st Qu.:3.3377 1st Qu.:2.565 1st Qu.:2.33214
## Median :-1.897 Median :4.0495 Median :2.833 Median :2.81512
## Mean :-1.864 Mean :4.0489 Mean :2.814 Mean :2.87534
## 3rd Qu.:-1.661 3rd Qu.:4.6857 3rd Qu.:3.045 3rd Qu.:3.55121
## Max. :-0.844 Max. :6.7959 Max. :4.007 Max. :5.35895
## MMP_3 MMP10 MMP7 Myoglobin
## Min. :-4.4228 Min. :-4.934 Min. :-8.3975 Min. :-3.1701
## 1st Qu.:-2.7489 1st Qu.:-3.938 1st Qu.:-4.8199 1st Qu.:-2.0402
## Median :-2.4534 Median :-3.650 Median :-3.7735 Median :-1.4697
## Mean :-2.4455 Mean :-3.635 Mean :-3.7894 Mean :-1.3671
## 3rd Qu.:-2.1203 3rd Qu.:-3.352 3rd Qu.:-2.7140 3rd Qu.:-0.7988
## Max. :-0.5276 Max. :-2.207 Max. :-0.2222 Max. : 1.7750
## NT_proBNP NrCAM Osteopontin PAI_1
## Min. :3.178 Min. :2.639 Min. :4.111 Min. :-0.99085
## 1st Qu.:4.350 1st Qu.:3.998 1st Qu.:4.963 1st Qu.:-0.16655
## Median :4.554 Median :4.394 Median :5.187 Median : 0.09396
## Mean :4.552 Mean :4.362 Mean :5.204 Mean : 0.07743
## 3rd Qu.:4.775 3rd Qu.:4.749 3rd Qu.:5.442 3rd Qu.: 0.32005
## Max. :5.886 Max. :6.011 Max. :6.308 Max. : 1.16611
## PAPP_A PLGF PYY Pancreatic_polypeptide
## Min. :-3.311 Min. :2.485 Min. :2.186 Min. :-2.12026
## 1st Qu.:-2.936 1st Qu.:3.638 1st Qu.:2.833 1st Qu.:-0.52763
## Median :-2.871 Median :3.871 Median :2.996 Median :-0.04082
## Mean :-2.854 Mean :3.912 Mean :3.015 Mean :-0.01323
## 3rd Qu.:-2.749 3rd Qu.:4.205 3rd Qu.:3.178 3rd Qu.: 0.53063
## Max. :-2.520 Max. :5.170 Max. :3.932 Max. : 1.93152
## Prolactin Prostatic_Acid_Phosphatase Protein_S
## Min. :-1.30933 Min. :-1.934 Min. :-3.338
## 1st Qu.:-0.13926 1st Qu.:-1.717 1st Qu.:-2.464
## Median : 0.00000 Median :-1.690 Median :-2.259
## Mean : 0.04495 Mean :-1.685 Mean :-2.240
## 3rd Qu.: 0.25799 3rd Qu.:-1.654 3rd Qu.:-2.000
## Max. : 0.99325 Max. :-1.424 Max. :-1.221
## Pulmonary_and_Activation_Regulat RANTES Resistin
## Min. :-2.5133 Min. :-7.222 Min. :-34.967
## 1st Qu.:-1.8326 1st Qu.:-6.725 1st Qu.:-21.468
## Median :-1.5141 Median :-6.502 Median :-17.466
## Mean :-1.4880 Mean :-6.511 Mean :-17.641
## 3rd Qu.:-1.1712 3rd Qu.:-6.320 3rd Qu.:-13.501
## Max. :-0.2744 Max. :-5.547 Max. : -2.239
## S100b SGOT SHBG SOD
## Min. :0.1874 Min. :-1.3471 Min. :-4.135 Min. :4.317
## 1st Qu.:1.0012 1st Qu.:-0.6349 1st Qu.:-2.813 1st Qu.:5.094
## Median :1.2544 Median :-0.4005 Median :-2.489 Median :5.366
## Mean :1.2505 Mean :-0.4057 Mean :-2.477 Mean :5.336
## 3rd Qu.:1.4996 3rd Qu.:-0.1985 3rd Qu.:-2.120 3rd Qu.:5.583
## Max. :2.3726 Max. : 0.7419 Max. :-1.109 Max. :6.317
## Serum_Amyloid_P Sortilin Stem_Cell_Factor TGF_alpha
## Min. :-7.506 Min. :1.654 Min. :2.251 Min. : 6.843
## 1st Qu.:-6.377 1st Qu.:3.343 1st Qu.:3.045 1st Qu.: 8.859
## Median :-6.032 Median :3.867 Median :3.296 Median : 9.919
## Mean :-6.017 Mean :3.852 Mean :3.301 Mean : 9.801
## 3rd Qu.:-5.655 3rd Qu.:4.371 3rd Qu.:3.526 3rd Qu.:10.695
## Max. :-4.646 Max. :6.225 Max. :4.277 Max. :13.827
## TIMP_1 TNF_RII TRAIL_R3 TTR_prealbumin
## Min. : 1.742 Min. :-1.6607 Min. :-1.2107 Min. :2.485
## 1st Qu.:10.490 1st Qu.:-0.8210 1st Qu.:-0.7008 1st Qu.:2.773
## Median :11.565 Median :-0.5978 Median :-0.5317 Median :2.833
## Mean :11.750 Mean :-0.5939 Mean :-0.5394 Mean :2.854
## 3rd Qu.:12.697 3rd Qu.:-0.3784 3rd Qu.:-0.3849 3rd Qu.:2.944
## Max. :18.881 Max. : 0.4700 Max. : 0.2694 Max. :3.332
## Tamm_Horsfall_Protein_THP Thrombomodulin Thrombopoietin
## Min. :-3.206 Min. :-2.0377 Min. :-1.53957
## 1st Qu.:-3.137 1st Qu.:-1.6256 1st Qu.:-0.88645
## Median :-3.117 Median :-1.4920 Median :-0.75100
## Mean :-3.116 Mean :-1.5050 Mean :-0.75419
## 3rd Qu.:-3.096 3rd Qu.:-1.3406 3rd Qu.:-0.62887
## Max. :-2.995 Max. :-0.8166 Max. : 0.09762
## Thymus_Expressed_Chemokine_TECK Thyroid_Stimulating_Hormone
## Min. :1.508 Min. :-6.190
## 1st Qu.:3.343 1st Qu.:-4.962
## Median :3.810 Median :-4.510
## Mean :3.848 Mean :-4.499
## 3rd Qu.:4.316 3rd Qu.:-4.017
## Max. :6.225 Max. :-1.715
## Thyroxine_Binding_Globulin Tissue_Factor Transferrin
## Min. :-2.4769 Min. :-0.2107 Min. :1.932
## 1st Qu.:-1.7720 1st Qu.: 0.8329 1st Qu.:2.708
## Median :-1.5141 Median : 1.2238 Median :2.890
## Mean :-1.4788 Mean : 1.1702 Mean :2.909
## 3rd Qu.:-1.2379 3rd Qu.: 1.4816 3rd Qu.:3.091
## Max. :-0.2107 Max. : 2.4849 Max. :3.761
## Trefoil_Factor_3_TFF3 VCAM_1 VEGF Vitronectin
## Min. :-4.744 Min. :1.723 Min. :11.83 Min. :-1.42712
## 1st Qu.:-4.135 1st Qu.:2.485 1st Qu.:15.77 1st Qu.:-0.51083
## Median :-3.863 Median :2.708 Median :17.08 Median :-0.30111
## Mean :-3.876 Mean :2.688 Mean :16.99 Mean :-0.28473
## 3rd Qu.:-3.650 3rd Qu.:2.890 3rd Qu.:18.10 3rd Qu.:-0.03564
## Max. :-2.957 Max. :3.689 Max. :22.38 Max. : 0.53063
## von_Willebrand_Factor Class E4 E3
## Min. :-4.991 Impaired: 73 Min. :1.000 Min. :1.000
## 1st Qu.:-4.200 Control :194 1st Qu.:1.000 1st Qu.:2.000
## Median :-3.912 Median :1.000 Median :2.000
## Mean :-3.906 Mean :1.401 Mean :1.918
## 3rd Qu.:-3.612 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :-2.957 Max. :2.000 Max. :2.000
## E2
## Min. :1.000
## 1st Qu.:1.000
## Median :1.000
## Mean :1.161
## 3rd Qu.:1.000
## Max. :2.000for (i in 1:ncol(PMA_PreModelling_Test)){
if (names(PMA_PreModelling_Test)[i]!="Class"){
PMA_PreModelling_Test[,i] <- as.numeric(PMA_PreModelling_Test[,i])
}
}
summary(PMA_PreModelling_Test)## ACE_CD143_Angiotensin_Converti ACTH_Adrenocorticotropic_Hormon
## Min. :-0.5473 Min. :-2.2073
## 1st Qu.: 0.9462 1st Qu.:-1.7148
## Median : 1.3013 Median :-1.5374
## Mean : 1.3105 Mean :-1.5311
## 3rd Qu.: 1.6320 3rd Qu.:-1.3863
## Max. : 3.0890 Max. :-0.7985
## AXL Adiponectin Alpha_1_Antichymotrypsin
## Min. :-0.73509 Min. :-7.059 Min. :0.1823
## 1st Qu.:-0.08175 1st Qu.:-5.737 1st Qu.:1.0647
## Median : 0.28035 Median :-5.360 Median :1.3083
## Mean : 0.25373 Mean :-5.298 Mean :1.3077
## 3rd Qu.: 0.60768 3rd Qu.:-4.917 3rd Qu.:1.5686
## Max. : 1.28634 Max. :-3.474 Max. :2.2192
## Alpha_1_Antitrypsin Alpha_1_Microglobulin Alpha_2_Macroglobulin
## Min. :-18.17 Min. :-4.135 Min. :-238.64
## 1st Qu.:-14.70 1st Qu.:-3.284 1st Qu.:-186.64
## Median :-13.59 Median :-3.006 Median :-162.93
## Mean :-13.49 Mean :-2.983 Mean :-162.89
## 3rd Qu.:-12.31 3rd Qu.:-2.674 3rd Qu.:-136.53
## Max. :-10.06 Max. :-1.897 Max. : -50.17
## Angiopoietin_2_ANG_2 Angiotensinogen Apolipoprotein_A_IV Apolipoprotein_A1
## Min. :-0.05129 Min. :1.710 Min. :-2.749 Min. :-8.568
## 1st Qu.: 0.35372 1st Qu.:2.068 1st Qu.:-2.186 1st Qu.:-7.818
## Median : 0.55921 Median :2.276 Median :-1.897 Median :-7.497
## Mean : 0.60278 Mean :2.274 Mean :-1.867 Mean :-7.488
## 3rd Qu.: 0.78846 3rd Qu.:2.430 3rd Qu.:-1.526 3rd Qu.:-7.176
## Max. : 1.77495 Max. :2.752 Max. :-1.109 Max. :-6.645
## Apolipoprotein_A2 Apolipoprotein_B Apolipoprotein_CI Apolipoprotein_CIII
## Min. :-1.9661 Min. :-8.192 Min. :-2.847 Min. :-3.863
## 1st Qu.:-0.9416 1st Qu.:-6.748 1st Qu.:-1.897 1st Qu.:-2.781
## Median :-0.7032 Median :-5.819 Median :-1.609 Median :-2.557
## Mean :-0.6902 Mean :-5.649 Mean :-1.625 Mean :-2.523
## 3rd Qu.:-0.3533 3rd Qu.:-4.603 3rd Qu.:-1.309 3rd Qu.:-2.231
## Max. : 0.5306 Max. :-2.339 Max. :-0.462 Max. :-1.386
## Apolipoprotein_D Apolipoprotein_E Apolipoprotein_H
## Min. :0.2624 Min. :0.6626 Min. :-1.1609
## 1st Qu.:1.1314 1st Qu.:2.1526 1st Qu.:-0.5317
## Median :1.3863 Median :2.8181 Median :-0.2897
## Mean :1.3943 Mean :2.7160 Mean :-0.3212
## 3rd Qu.:1.6864 3rd Qu.:3.2363 3rd Qu.:-0.1032
## Max. :2.6391 Max. :4.6844 Max. : 0.4402
## B_Lymphocyte_Chemoattractant_BL BMP_6 Beta_2_Microglobulin
## Min. :0.7318 Min. :-2.669 Min. :-0.51083
## 1st Qu.:1.5304 1st Qu.:-2.152 1st Qu.:-0.06188
## Median :1.8528 Median :-1.964 Median : 0.18232
## Mean :1.8766 Mean :-1.937 Mean : 0.15566
## 3rd Qu.:2.3714 3rd Qu.:-1.675 3rd Qu.: 0.40547
## Max. :2.9757 Max. :-1.181 Max. : 0.83291
## Betacellulin C_Reactive_Protein CD40 CD5L
## Min. :32.00 Min. :-8.112 Min. :-1.9390 Min. :-1.96611
## 1st Qu.:46.00 1st Qu.:-6.725 1st Qu.:-1.4420 1st Qu.:-0.36747
## Median :51.00 Median :-6.166 Median :-1.2574 Median :-0.05135
## Mean :52.74 Mean :-5.997 Mean :-1.2773 Mean :-0.08760
## 3rd Qu.:59.75 3rd Qu.:-5.369 3rd Qu.:-1.1034 3rd Qu.: 0.24235
## Max. :80.00 Max. :-3.411 Max. :-0.7766 Max. : 0.91629
## Calbindin Calcitonin CgA Clusterin_Apo_J
## Min. :10.81 Min. :-0.7134 Min. :166.6 Min. :1.932
## 1st Qu.:18.88 1st Qu.: 1.2014 1st Qu.:268.2 1st Qu.:2.565
## Median :21.06 Median : 1.6849 Median :324.7 Median :2.833
## Mean :21.49 Mean : 1.7250 Mean :320.2 Mean :2.845
## 3rd Qu.:24.00 3rd Qu.: 2.2618 3rd Qu.:362.3 3rd Qu.:3.045
## Max. :35.36 Max. : 4.1109 Max. :494.5 Max. :3.761
## Complement_3 Complement_Factor_H Connective_Tissue_Growth_Factor
## Min. :-22.40 Min. :0.2766 Min. :0.09531
## 1st Qu.:-17.50 1st Qu.:2.6019 1st Qu.:0.58779
## Median :-15.90 Median :3.3983 Median :0.74194
## Mean :-15.91 Mean :3.3897 Mean :0.74507
## 3rd Qu.:-14.34 3rd Qu.:4.2548 3rd Qu.:0.87547
## Max. :-10.23 Max. :6.5597 Max. :1.41099
## Cortisol Creatine_Kinase_MB Cystatin_C EGF_R
## Min. : 0.10 Min. :-1.872 Min. :7.728 Min. :-1.2694
## 1st Qu.: 8.90 1st Qu.:-1.721 1st Qu.:8.301 1st Qu.:-0.8859
## Median :10.00 Median :-1.651 Median :8.544 Median :-0.6917
## Mean :10.46 Mean :-1.652 Mean :8.576 Mean :-0.6965
## 3rd Qu.:12.00 3rd Qu.:-1.590 3rd Qu.:8.837 3rd Qu.:-0.5034
## Max. :22.00 Max. :-1.434 Max. :9.694 Max. : 0.1891
## EN_RAGE ENA_78 Eotaxin_3 FAS
## Min. :-8.3774 Min. :-1.405 Min. : 23.00 Min. :-1.1087
## 1st Qu.:-4.1836 1st Qu.:-1.382 1st Qu.: 43.00 1st Qu.:-0.7133
## Median :-3.6889 Median :-1.374 Median : 54.00 Median :-0.5798
## Mean :-3.5986 Mean :-1.376 Mean : 55.55 Mean :-0.5414
## 3rd Qu.:-3.2189 3rd Qu.:-1.368 3rd Qu.: 64.00 3rd Qu.:-0.3355
## Max. :-0.8675 Max. :-1.353 Max. :107.00 Max. : 0.1823
## FSH_Follicle_Stimulation_Hormon Fas_Ligand Fatty_Acid_Binding_Protein
## Min. :-1.8101 Min. :0.288 Min. :-0.4559
## 1st Qu.:-1.2694 1st Qu.:2.073 1st Qu.: 0.7998
## Median :-0.9763 Median :2.665 Median : 1.1866
## Mean :-1.0597 Mean :2.649 Mean : 1.2884
## 3rd Qu.:-0.8068 3rd Qu.:3.162 3rd Qu.: 1.9192
## Max. :-0.4757 Max. :5.377 Max. : 3.2188
## Ferritin Fetuin_A Fibrinogen GRO_alpha
## Min. :0.8983 Min. :0.5306 Min. :-9.373 Min. :1.271
## 1st Qu.:2.1473 1st Qu.:1.0296 1st Qu.:-7.799 1st Qu.:1.351
## Median :2.6260 Median :1.3083 Median :-7.316 Median :1.372
## Mean :2.7069 Mean :1.3116 Mean :-7.360 Mean :1.378
## 3rd Qu.:3.1672 3rd Qu.:1.6094 3rd Qu.:-6.970 3rd Qu.:1.398
## Max. :4.9282 Max. :2.2083 Max. :-6.166 Max. :1.514
## Gamma_Interferon_induced_Monokin Glutathione_S_Transferase_alpha
## Min. :2.545 Min. :0.5661
## 1st Qu.:2.698 1st Qu.:0.8257
## Median :2.768 Median :0.9493
## Mean :2.772 Mean :0.9440
## 3rd Qu.:2.829 3rd Qu.:1.0457
## Max. :3.046 Max. :1.3102
## HB_EGF HCC_4 Hepatocyte_Growth_Factor_HGF I_309
## Min. : 3.521 Min. :-4.343 Min. :-0.61619 Min. :2.041
## 1st Qu.: 5.949 1st Qu.:-3.772 1st Qu.:-0.05661 1st Qu.:2.724
## Median : 6.980 Median :-3.540 Median : 0.18232 Median :2.944
## Mean : 6.844 Mean :-3.538 Mean : 0.18076 Mean :2.921
## 3rd Qu.: 7.745 3rd Qu.:-3.352 3rd Qu.: 0.33647 3rd Qu.:3.135
## Max. :10.359 Max. :-2.489 Max. : 1.09861 Max. :3.689
## ICAM_1 IGF_BP_2 IL_11 IL_13
## Min. :-1.4661 Min. :4.718 Min. :2.031 Min. :1.232
## 1st Qu.:-0.7671 1st Qu.:5.127 1st Qu.:3.960 1st Qu.:1.274
## Median :-0.5903 Median :5.255 Median :4.838 Median :1.283
## Mean :-0.5958 Mean :5.263 Mean :4.651 Mean :1.283
## 3rd Qu.:-0.3574 3rd Qu.:5.402 3rd Qu.:5.482 3rd Qu.:1.292
## Max. : 0.3602 Max. :5.916 Max. :8.692 Max. :1.310
## IL_16 IL_17E IL_1alpha IL_3
## Min. :0.9568 Min. :1.582 Min. :-8.468 Min. :-5.521
## 1st Qu.:2.4411 1st Qu.:3.637 1st Qu.:-7.849 1st Qu.:-4.324
## Median :2.8763 Median :4.723 Median :-7.562 Median :-3.963
## Mean :2.8176 Mean :4.774 Mean :-7.549 Mean :-3.976
## 3rd Qu.:3.3514 3rd Qu.:5.415 3rd Qu.:-7.279 3rd Qu.:-3.576
## Max. :4.1028 Max. :8.081 Max. :-6.377 Max. :-3.079
## IL_4 IL_5 IL_6 IL_6_Receptor
## Min. :0.5306 Min. :-1.04982 Min. :-1.53428 Min. :-0.74560
## 1st Qu.:1.4586 1st Qu.:-0.03062 1st Qu.:-0.40924 1st Qu.:-0.20131
## Median :1.7226 Median : 0.22234 Median :-0.07205 Median : 0.00000
## Mean :1.7445 Mean : 0.22853 Mean :-0.05216 Mean : 0.06213
## 3rd Qu.:2.0669 3rd Qu.: 0.53063 3rd Qu.: 0.34805 3rd Qu.: 0.27297
## Max. :2.7081 Max. : 1.13140 Max. : 1.00562 Max. : 0.77048
## IL_7 IL_8 IP_10_Inducible_Protein_10 IgA
## Min. :1.310 Min. :1.615 Min. :4.263 Min. :-7.621
## 1st Qu.:2.379 1st Qu.:1.684 1st Qu.:5.323 1st Qu.:-6.571
## Median :3.148 Median :1.702 Median :5.617 Median :-6.012
## Mean :3.143 Mean :1.704 Mean :5.636 Mean :-6.066
## 3rd Qu.:3.706 3rd Qu.:1.725 3rd Qu.:5.917 3rd Qu.:-5.606
## Max. :5.000 Max. :1.836 Max. :7.208 Max. :-4.733
## Insulin Kidney_Injury_Molecule_1_KIM_1 LOX_1
## Min. :-2.0099 Min. :-1.251 Min. :0.0000
## 1st Qu.:-1.4466 1st Qu.:-1.209 1st Qu.:0.9649
## Median :-1.2169 Median :-1.187 Median :1.2238
## Mean :-1.1998 Mean :-1.188 Mean :1.2085
## 3rd Qu.:-1.0105 3rd Qu.:-1.166 3rd Qu.:1.4351
## Max. :-0.5025 Max. :-1.124 Max. :2.3979
## Leptin Lipoprotein_a MCP_1 MCP_2
## Min. :-1.9471 Min. :-6.571 Min. :5.889 Min. :0.4006
## 1st Qu.:-1.6334 1st Qu.:-5.116 1st Qu.:6.318 1st Qu.:1.5304
## Median :-1.4294 Median :-4.657 Median :6.482 Median :1.8528
## Mean :-1.4363 Mean :-4.515 Mean :6.480 Mean :1.8104
## 3rd Qu.:-1.2409 3rd Qu.:-4.017 3rd Qu.:6.627 3rd Qu.:2.0827
## Max. :-0.8387 Max. :-2.040 Max. :7.065 Max. :3.7545
## MIF MIP_1alpha MIP_1beta MMP_2
## Min. :-2.797 Min. :1.008 Min. :1.917 Min. :0.6248
## 1st Qu.:-2.120 1st Qu.:3.302 1st Qu.:2.485 1st Qu.:2.5513
## Median :-1.966 Median :3.736 Median :2.773 Median :2.9937
## Mean :-1.932 Mean :3.898 Mean :2.784 Mean :3.0347
## 3rd Qu.:-1.715 3rd Qu.:4.686 3rd Qu.:3.079 3rd Qu.:3.4798
## Max. :-1.109 Max. :5.735 Max. :3.784 Max. :6.0996
## MMP_3 MMP10 MMP7 Myoglobin
## Min. :-3.650 Min. :-4.948 Min. :-7.5346 Min. :-3.2968
## 1st Qu.:-2.852 1st Qu.:-4.075 1st Qu.:-4.9634 1st Qu.:-2.0217
## Median :-2.532 Median :-3.612 Median :-4.0302 Median :-1.5874
## Mean :-2.490 Mean :-3.676 Mean :-4.0148 Mean :-1.4165
## 3rd Qu.:-2.120 3rd Qu.:-3.331 3rd Qu.:-3.1640 3rd Qu.:-0.7765
## Max. :-1.171 Max. :-2.900 Max. :-0.1953 Max. : 1.1314
## NT_proBNP NrCAM Osteopontin PAI_1
## Min. :3.611 Min. :2.890 Min. :4.078 Min. :-0.990849
## 1st Qu.:4.174 1st Qu.:3.871 1st Qu.:4.892 1st Qu.:-0.334043
## Median :4.477 Median :4.317 Median :5.168 Median : 0.000000
## Mean :4.488 Mean :4.291 Mean :5.177 Mean :-0.003947
## 3rd Qu.:4.794 3rd Qu.:4.725 3rd Qu.:5.410 3rd Qu.: 0.303112
## Max. :5.398 Max. :5.690 Max. :6.315 Max. : 0.885785
## PAPP_A PLGF PYY Pancreatic_polypeptide
## Min. :-3.152 Min. :2.639 Min. :2.398 Min. :-1.609438
## 1st Qu.:-2.971 1st Qu.:3.689 1st Qu.:2.833 1st Qu.:-0.506693
## Median :-2.841 Median :3.892 Median :2.996 Median : 0.138816
## Mean :-2.845 Mean :3.884 Mean :2.976 Mean :-0.005258
## 3rd Qu.:-2.719 3rd Qu.:4.123 3rd Qu.:3.178 3rd Qu.: 0.470004
## Max. :-2.488 Max. :4.710 Max. :3.738 Max. : 1.504077
## Prolactin Prostatic_Acid_Phosphatase Protein_S
## Min. :-0.38566 Min. :-1.800 Min. :-3.154
## 1st Qu.:-0.16558 1st Qu.:-1.739 1st Qu.:-2.579
## Median : 0.00000 Median :-1.690 Median :-2.259
## Mean : 0.05195 Mean :-1.692 Mean :-2.268
## 3rd Qu.: 0.18232 3rd Qu.:-1.659 3rd Qu.:-1.924
## Max. : 0.78846 Max. :-1.540 Max. :-1.547
## Pulmonary_and_Activation_Regulat RANTES Resistin
## Min. :-2.4418 Min. :-7.236 Min. :-30.156
## 1st Qu.:-1.8326 1st Qu.:-6.725 1st Qu.:-22.131
## Median :-1.5141 Median :-6.571 Median :-18.014
## Mean :-1.5007 Mean :-6.540 Mean :-18.245
## 3rd Qu.:-1.1712 3rd Qu.:-6.392 3rd Qu.:-15.202
## Max. :-0.4463 Max. :-5.843 Max. : -6.594
## S100b SGOT SHBG SOD
## Min. :0.1874 Min. :-1.8971 Min. :-3.730 Min. :4.382
## 1st Qu.:0.9600 1st Qu.:-0.7498 1st Qu.:-3.052 1st Qu.:5.006
## Median :1.1571 Median :-0.4780 Median :-2.711 Median :5.313
## Mean :1.1819 Mean :-0.4898 Mean :-2.686 Mean :5.302
## 3rd Qu.:1.3807 3rd Qu.:-0.2138 3rd Qu.:-2.343 3rd Qu.:5.547
## Max. :2.1950 Max. : 0.1823 Max. :-1.561 Max. :6.461
## Serum_Amyloid_P Sortilin Stem_Cell_Factor TGF_alpha
## Min. :-7.182 Min. :1.508 Min. :2.219 Min. : 7.500
## 1st Qu.:-6.438 1st Qu.:3.177 1st Qu.:3.045 1st Qu.: 9.062
## Median :-6.215 Median :3.867 Median :3.314 Median : 9.596
## Mean :-6.083 Mean :3.787 Mean :3.267 Mean : 9.776
## 3rd Qu.:-5.607 3rd Qu.:4.371 3rd Qu.:3.466 3rd Qu.:10.612
## Max. :-4.699 Max. :5.681 Max. :4.078 Max. :13.083
## TIMP_1 TNF_RII TRAIL_R3 TTR_prealbumin
## Min. : 8.198 Min. :-1.6607 Min. :-1.30636 Min. :2.485
## 1st Qu.:10.530 1st Qu.:-0.8675 1st Qu.:-0.73332 1st Qu.:2.773
## Median :11.341 Median :-0.6541 Median :-0.55547 Median :2.890
## Mean :11.520 Mean :-0.6270 Mean :-0.58640 Mean :2.854
## 3rd Qu.:12.352 3rd Qu.:-0.3320 3rd Qu.:-0.47065 3rd Qu.:2.944
## Max. :16.547 Max. : 0.4055 Max. : 0.09622 Max. :3.091
## Tamm_Horsfall_Protein_THP Thrombomodulin Thrombopoietin
## Min. :-3.206 Min. :-2.054 Min. :-1.5396
## 1st Qu.:-3.144 1st Qu.:-1.675 1st Qu.:-0.8383
## Median :-3.126 Median :-1.534 Median :-0.7039
## Mean :-3.123 Mean :-1.533 Mean :-0.7192
## 3rd Qu.:-3.101 3rd Qu.:-1.341 3rd Qu.:-0.6289
## Max. :-3.041 Max. :-1.019 Max. :-0.3029
## Thymus_Expressed_Chemokine_TECK Thyroid_Stimulating_Hormone
## Min. :2.141 Min. :-6.190
## 1st Qu.:3.283 1st Qu.:-4.733
## Median :3.753 Median :-4.269
## Mean :3.770 Mean :-4.221
## 3rd Qu.:4.316 3rd Qu.:-3.828
## Max. :5.681 Max. :-2.040
## Thyroxine_Binding_Globulin Tissue_Factor Transferrin
## Min. :-2.3026 Min. :0.0000 Min. :2.282
## 1st Qu.:-1.7148 1st Qu.:0.7053 1st Qu.:2.708
## Median :-1.4919 Median :1.1473 Median :2.890
## Mean :-1.4902 Mean :1.1356 Mean :2.900
## 3rd Qu.:-1.2379 3rd Qu.:1.5149 3rd Qu.:3.135
## Max. :-0.5978 Max. :2.7081 Max. :3.497
## Trefoil_Factor_3_TFF3 VCAM_1 VEGF Vitronectin
## Min. :-4.906 Min. :2.028 Min. :12.23 Min. :-1.07881
## 1st Qu.:-4.200 1st Qu.:2.420 1st Qu.:15.03 1st Qu.:-0.46204
## Median :-3.912 Median :2.674 Median :17.08 Median :-0.28106
## Mean :-3.947 Mean :2.644 Mean :16.70 Mean :-0.26833
## 3rd Qu.:-3.772 3rd Qu.:2.833 3rd Qu.:18.19 3rd Qu.:-0.05394
## Max. :-3.170 Max. :3.466 Max. :21.18 Max. : 0.40547
## von_Willebrand_Factor Class E4 E3
## Min. :-4.920 Impaired:18 Min. :1.000 Min. :1.000
## 1st Qu.:-4.269 Control :48 1st Qu.:1.000 1st Qu.:2.000
## Median :-4.017 Median :1.000 Median :2.000
## Mean :-4.014 Mean :1.303 Mean :1.985
## 3rd Qu.:-3.730 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :-3.058 Max. :2.000 Max. :2.000
## E2
## Min. :1.000
## 1st Qu.:1.000
## Median :1.000
## Mean :1.061
## 3rd Qu.:1.000
## Max. :2.000##################################
# Formulating a function to summarize
# model performance metrics
##################################
FiveMetricsSummary <- function(...) c(twoClassSummary(...), defaultSummary(...))
##################################
# Creating consistent fold assignments
# for the Cross Validation process
##################################
set.seed(12345678)
KFold_Indices <- createFolds(PMA_PreModelling_Train$Class ,
k = 10,
returnTrain=TRUE)
##################################
# Creating a range of
# variable subsets for evaluation
##################################
VariableSubset <- seq(1, length(names(PMA_PreModelling_Train))-2, by=20)
##################################
# Formulating the controls for the
# recursive feature elimination process
##################################
KFold_RFEControl <- rfeControl(method = "cv",
saveDetails = TRUE,
index = KFold_Indices,
returnResamp = "final")
##################################
# Formulating the controls for the
# model training process
##################################
KFold_TrainControl <- trainControl(method = "cv",
summaryFunction = FiveMetricsSummary,
classProbs = TRUE,
index = KFold_Indices)
##################################
# Running the random forest model
# by setting the caret method to 'rf'
##################################
set.seed(12345678)
RF_FULL_Tune <- caret::train(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
method = "rf",
metric = "ROC",
tuneGrid = data.frame(mtry = floor(sqrt(length(names(PMA_PreModelling_Train) %in% c("Class"))))),
ntree = 100,
trControl = KFold_TrainControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
RF_FULL_Tune## Random Forest
##
## 267 samples
## 127 predictors
## 2 classes: 'Impaired', 'Control'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 240, 239, 241, 240, 241, 241, ...
## Resampling results:
##
## ROC Sens Spec Accuracy Kappa
## 0.7826833 0.3035714 0.9589474 0.7797517 0.3094392
##
## Tuning parameter 'mtry' was held constant at a value of 11RF_FULL_Tune$finalModel##
## Call:
## randomForest(x = x, y = y, ntree = 100, mtry = param$mtry)
## Type of random forest: classification
## Number of trees: 100
## No. of variables tried at each split: 11
##
## OOB estimate of error rate: 23.22%
## Confusion matrix:
## Impaired Control class.error
## Impaired 22 51 0.69863014
## Control 11 183 0.05670103RF_FULL_Tune$results## mtry ROC Sens Spec Accuracy Kappa ROCSD SensSD
## 1 11 0.7826833 0.3035714 0.9589474 0.7797517 0.3094392 0.1152203 0.191404
## SpecSD AccuracySD KappaSD
## 1 0.03201935 0.06273494 0.225028(RF_FULL_Train_ROCCurveAUC <- RF_FULL_Tune$results[,c("ROC")])## [1] 0.7826833##################################
# Identifying and plotting the
# best model predictors
##################################
RF_FULL_VarImp <- varImp(RF_FULL_Tune, scale = TRUE)
plot(RF_FULL_VarImp,
top=25,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Random Forest",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
RF_FULL_Test <- data.frame(RF_FULL_Observed = PMA_PreModelling_Test$Class,
RF_FULL_Predicted = predict(RF_FULL_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
RF_FULL_Test_ROC <- roc(response = RF_FULL_Test$RF_FULL_Observed,
predictor = RF_FULL_Test$RF_FULL_Predicted.Impaired,
levels = rev(levels(RF_FULL_Test$RF_FULL_Observed)))
(RF_FULL_Test_ROCCurveAUC <- auc(RF_FULL_Test_ROC)[1])## [1] 0.79803241.5.2 Linear Discriminant Analysis Without RFE (LDA_FULL)
Linear Discriminant Analysis finds a linear combination of features that best separates the classes in a data set by projecting the data onto a lower-dimensional space that maximizes the separation between the classes. The algorithm searches for a set of linear discriminants that maximize the ratio of between-class variance to within-class variance by evaluating directions in the feature space that best separate the different classes of data. LDA assumes that the data has a Gaussian distribution and that the covariance matrices of the different classes are equal, in addition to the data being linearly separable by the presence of a linear decision boundary can accurately classify the different classes.
[A] The linear discriminant analysis model from the MASS package was implemented without recursive feature elimination through the caret package.
[B] The model does not contain any hyperparameter.
[C] The cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration is fixed due to the absence of a hyperparameter
[C.2] ROC Curve AUC = 0.80151
[D] The model does not allow for ranking of predictors in terms of variable importance.
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.77199
##################################
# Running the linear discriminant analysis model
# by setting the caret method to 'lda'
##################################
set.seed(12345678)
LDA_FULL_Tune <- caret::train(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
method = "lda",
metric = "ROC",
tol = 1.0e-12,
trControl = KFold_TrainControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
LDA_FULL_Tune## Linear Discriminant Analysis
##
## 267 samples
## 127 predictors
## 2 classes: 'Impaired', 'Control'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 240, 239, 241, 240, 241, 241, ...
## Resampling results:
##
## ROC Sens Spec Accuracy Kappa
## 0.8015132 0.6428571 0.8142105 0.7675417 0.4377465(LDA_FULL_Train_ROCCurveAUC <- LDA_FULL_Tune$results[,c("ROC")])## [1] 0.8015132##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
LDA_FULL_Test <- data.frame(LDA_FULL_Observed = PMA_PreModelling_Test$Class,
LDA_FULL_Predicted = predict(LDA_FULL_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
LDA_FULL_Test_ROC <- roc(response = LDA_FULL_Test$LDA_FULL_Observed,
predictor = LDA_FULL_Test$LDA_FULL_Predicted.Impaired,
levels = rev(levels(LDA_FULL_Test$LDA_FULL_Observed)))
(LDA_FULL_Test_ROCCurveAUC <- auc(LDA_FULL_Test_ROC)[1])## [1] 0.77199071.5.3 Naive Bayes Without RFE (NB_FULL)
Naive Bayes Classifier categorizes instances by applying Bayes Theorem in determining posterior probabilities as conditioned by the likelihood of features, and prior probabilities pertaining to both events and features. The algorithm naively assumes independence between features and assigns the same weight (degree of significance) to all given features.
[A] The naive bayes model from the klaR package was implemented without recursive feature elimination through the caret package.
[B] The model contains 3 hyperparameters:
[B.1] fL = laplace correction held constant at a value of 0
[B.2] adjust = bandwidth adjustment held constant at a value of TRUE
[B.3] usekernel = distribution type made to vary across a range of levels equal to TRUE and FALSE
[C] The cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves fL=0, adjust=TRUE and usekernel=TRUE
[C.2] ROC Curve AUC = 0.73904
[D] The model does not allow for ranking of predictors in terms of variable importance.
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.68055
##################################
# Running the naive bayes model
# by setting the caret method to 'nb'
##################################
set.seed(12345678)
NB_FULL_Tune <- caret::train(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
method = "nb",
metric = "ROC",
trControl = KFold_TrainControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
NB_FULL_Tune## Naive Bayes
##
## 267 samples
## 127 predictors
## 2 classes: 'Impaired', 'Control'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 240, 239, 241, 240, 241, 241, ...
## Resampling results across tuning parameters:
##
## usekernel ROC Sens Spec Accuracy Kappa
## FALSE 0.7333788 0.6089286 0.7363158 0.7006410 0.3162972
## TRUE 0.7390414 0.5821429 0.7518421 0.7043549 0.3064041
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
## = 1.NB_FULL_Tune$finalModel## $apriori
## grouping
## Impaired Control
## 0.2734082 0.7265918
##
## $tables
## $tables$ACE_CD143_Angiotensin_Converti
## $tables$ACE_CD143_Angiotensin_Converti$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1998
##
## x y
## Min. :-0.2455 Min. :0.0003239
## 1st Qu.: 0.5589 1st Qu.:0.0491549
## Median : 1.3633 Median :0.3464146
## Mean : 1.3633 Mean :0.3104626
## 3rd Qu.: 2.1677 3rd Qu.:0.5465936
## Max. : 2.9721 Max. :0.6285079
##
## $tables$ACE_CD143_Angiotensin_Converti$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1681
##
## x y
## Min. :-1.17998 Min. :0.0001383
## 1st Qu.:-0.04894 1st Qu.:0.0089653
## Median : 1.08210 Median :0.1031038
## Mean : 1.08210 Mean :0.2208171
## 3rd Qu.: 2.21314 3rd Qu.:0.4102960
## Max. : 3.34418 Max. :0.7157811
##
##
## $tables$ACTH_Adrenocorticotropic_Hormon
## $tables$ACTH_Adrenocorticotropic_Hormon$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1143
##
## x y
## Min. :-2.5502 Min. :0.0005525
## 1st Qu.:-2.0438 1st Qu.:0.0694099
## Median :-1.5374 Median :0.3845342
## Mean :-1.5374 Mean :0.4931633
## 3rd Qu.:-1.0310 3rd Qu.:0.9231961
## Max. :-0.5246 Max. :1.2611194
##
## $tables$ACTH_Adrenocorticotropic_Hormon$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08486
##
## x y
## Min. :-2.4619 Min. :0.0003168
## 1st Qu.:-1.9937 1st Qu.:0.0897091
## Median :-1.5256 Median :0.2985430
## Mean :-1.5256 Mean :0.5335206
## 3rd Qu.:-1.0575 3rd Qu.:1.0773896
## Max. :-0.5894 Max. :1.3578535
##
##
## $tables$AXL
## $tables$AXL$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1452
##
## x y
## Min. :-0.73867 Min. :0.0004241
## 1st Qu.:-0.06473 1st Qu.:0.0590702
## Median : 0.60921 Median :0.2323831
## Mean : 0.60921 Mean :0.3705691
## 3rd Qu.: 1.28315 3rd Qu.:0.6520214
## Max. : 1.95709 Max. :1.0527190
##
## $tables$AXL$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1448
##
## x y
## Min. :-1.3574 Min. :0.0001606
## 1st Qu.:-0.5580 1st Qu.:0.0345472
## Median : 0.2415 Median :0.2153196
## Mean : 0.2415 Mean :0.3124200
## 3rd Qu.: 1.0409 3rd Qu.:0.6343461
## Max. : 1.8403 Max. :0.8441812
##
##
## $tables$Adiponectin
## $tables$Adiponectin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2539
##
## x y
## Min. :-7.264 Min. :0.0002544
## 1st Qu.:-6.170 1st Qu.:0.0395201
## Median :-5.076 Median :0.2068785
## Mean :-5.076 Mean :0.2282841
## 3rd Qu.:-3.982 3rd Qu.:0.3946243
## Max. :-2.888 Max. :0.5535765
##
## $tables$Adiponectin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.2047
##
## x y
## Min. :-7.340 Min. :0.0002701
## 1st Qu.:-6.228 1st Qu.:0.0422510
## Median :-5.116 Median :0.1470730
## Mean :-5.116 Mean :0.2246300
## 3rd Qu.:-4.004 3rd Qu.:0.4161987
## Max. :-2.892 Max. :0.6307886
##
##
## $tables$Alpha_1_Antichymotrypsin
## $tables$Alpha_1_Antichymotrypsin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.137
##
## x y
## Min. :-0.005415 Min. :0.0004498
## 1st Qu.: 0.674305 1st Qu.:0.0430254
## Median : 1.354025 Median :0.2039661
## Mean : 1.354025 Mean :0.3674222
## 3rd Qu.: 2.033745 3rd Qu.:0.7063763
## Max. : 2.713465 Max. :1.0983017
##
## $tables$Alpha_1_Antichymotrypsin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1039
##
## x y
## Min. :-0.04928 Min. :0.0002244
## 1st Qu.: 0.60377 1st Qu.:0.0338283
## Median : 1.25683 Median :0.1890650
## Mean : 1.25683 Mean :0.3824361
## 3rd Qu.: 1.90988 3rd Qu.:0.7166598
## Max. : 2.56294 Max. :1.1204953
##
##
## $tables$Alpha_1_Antitrypsin
## $tables$Alpha_1_Antitrypsin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.4782
##
## x y
## Min. :-17.980 Min. :0.0001305
## 1st Qu.:-15.174 1st Qu.:0.0145353
## Median :-12.369 Median :0.0298382
## Mean :-12.369 Mean :0.0890120
## 3rd Qu.: -9.563 3rd Qu.:0.1575761
## Max. : -6.757 Max. :0.2896921
##
## $tables$Alpha_1_Antitrypsin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.4461
##
## x y
## Min. :-18.367 Min. :5.293e-05
## 1st Qu.:-15.545 1st Qu.:6.378e-03
## Median :-12.723 Median :4.268e-02
## Mean :-12.723 Mean :8.850e-02
## 3rd Qu.: -9.901 3rd Qu.:1.640e-01
## Max. : -7.079 Max. :2.907e-01
##
##
## $tables$Alpha_1_Microglobulin
## $tables$Alpha_1_Microglobulin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1673
##
## x y
## Min. :-4.414 Min. :0.0003731
## 1st Qu.:-3.643 1st Qu.:0.0406776
## Median :-2.872 Median :0.2126004
## Mean :-2.872 Mean :0.3240188
## 3rd Qu.:-2.102 3rd Qu.:0.6332765
## Max. :-1.331 Max. :0.8451424
##
## $tables$Alpha_1_Microglobulin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1525
##
## x y
## Min. :-4.800 Min. :0.0001518
## 1st Qu.:-3.929 1st Qu.:0.0221158
## Median :-3.057 Median :0.1811140
## Mean :-3.057 Mean :0.2865991
## 3rd Qu.:-2.186 3rd Qu.:0.5645652
## Max. :-1.315 Max. :0.8171640
##
##
## $tables$Alpha_2_Macroglobulin
## $tables$Alpha_2_Macroglobulin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 11.24
##
## x y
## Min. :-287.01 Min. :5.529e-06
## 1st Qu.:-221.69 1st Qu.:6.894e-04
## Median :-156.37 Median :1.898e-03
## Mean :-156.37 Mean :3.824e-03
## 3rd Qu.: -91.06 3rd Qu.:6.274e-03
## Max. : -25.74 Max. :1.128e-02
##
## $tables$Alpha_2_Macroglobulin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 11.74
##
## x y
## Min. :-324.9 Min. :1.973e-06
## 1st Qu.:-251.7 1st Qu.:2.986e-04
## Median :-178.5 Median :2.375e-03
## Mean :-178.5 Mean :3.412e-03
## 3rd Qu.:-105.3 3rd Qu.:6.144e-03
## Max. : -32.1 Max. :9.781e-03
##
##
## $tables$Angiopoietin_2_ANG_2
## $tables$Angiopoietin_2_ANG_2$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.0982
##
## x y
## Min. :-0.3459 Min. :0.0006289
## 1st Qu.: 0.1670 1st Qu.:0.0782649
## Median : 0.6798 Median :0.3465469
## Mean : 0.6798 Mean :0.4869656
## 3rd Qu.: 1.1927 3rd Qu.:0.8358893
## Max. : 1.7056 Max. :1.3565579
##
## $tables$Angiopoietin_2_ANG_2$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1062
##
## x y
## Min. :-0.8633 Min. :0.0002646
## 1st Qu.:-0.1863 1st Qu.:0.0253712
## Median : 0.4907 Median :0.1524206
## Mean : 0.4907 Mean :0.3689269
## 3rd Qu.: 1.1676 3rd Qu.:0.7127725
## Max. : 1.8446 Max. :1.1418247
##
##
## $tables$Angiotensinogen
## $tables$Angiotensinogen$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.0834
##
## x y
## Min. :1.501 Min. :0.001479
## 1st Qu.:1.879 1st Qu.:0.165084
## Median :2.257 Median :0.614894
## Mean :2.257 Mean :0.660679
## 3rd Qu.:2.635 3rd Qu.:1.032140
## Max. :3.013 Max. :1.737874
##
## $tables$Angiotensinogen$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.07902
##
## x y
## Min. :1.515 Min. :0.0004997
## 1st Qu.:1.915 1st Qu.:0.1124742
## Median :2.316 Median :0.5068715
## Mean :2.316 Mean :0.6230124
## 3rd Qu.:2.717 3rd Qu.:1.1739415
## Max. :3.118 Max. :1.4062447
##
##
## $tables$Apolipoprotein_A_IV
## $tables$Apolipoprotein_A_IV$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1366
##
## x y
## Min. :-3.3662 Min. :0.0004518
## 1st Qu.:-2.7071 1st Qu.:0.0524516
## Median :-2.0480 Median :0.2234621
## Mean :-2.0480 Mean :0.3789141
## 3rd Qu.:-1.3889 3rd Qu.:0.6986909
## Max. :-0.7298 Max. :1.1146797
##
## $tables$Apolipoprotein_A_IV$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1196
##
## x y
## Min. :-3.2593 Min. :0.0001954
## 1st Qu.:-2.5489 1st Qu.:0.0361127
## Median :-1.8385 Median :0.1922685
## Mean :-1.8385 Mean :0.3515544
## 3rd Qu.:-1.1281 3rd Qu.:0.6814906
## Max. :-0.4176 Max. :0.9546627
##
##
## $tables$Apolipoprotein_A1
## $tables$Apolipoprotein_A1$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1467
##
## x y
## Min. :-9.009 Min. :0.0004419
## 1st Qu.:-8.289 1st Qu.:0.0621708
## Median :-7.570 Median :0.2579061
## Mean :-7.570 Mean :0.3471858
## 3rd Qu.:-6.851 3rd Qu.:0.5748067
## Max. :-6.131 Max. :1.0407612
##
## $tables$Apolipoprotein_A1$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1383
##
## x y
## Min. :-9.095 Min. :0.000172
## 1st Qu.:-8.259 1st Qu.:0.035601
## Median :-7.423 Median :0.160004
## Mean :-7.423 Mean :0.298779
## 3rd Qu.:-6.587 3rd Qu.:0.593804
## Max. :-5.751 Max. :0.911416
##
##
## $tables$Apolipoprotein_A2
## $tables$Apolipoprotein_A2$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1786
##
## x y
## Min. :-2.0498 Min. :0.0005419
## 1st Qu.:-1.3021 1st Qu.:0.0569848
## Median :-0.5543 Median :0.3098914
## Mean :-0.5543 Mean :0.3339940
## 3rd Qu.: 0.1934 3rd Qu.:0.5963563
## Max. : 0.9412 Max. :0.7387526
##
## $tables$Apolipoprotein_A2$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1456
##
## x y
## Min. :-2.3338 Min. :0.0001601
## 1st Qu.:-1.4023 1st Qu.:0.0297597
## Median :-0.4708 Median :0.1434353
## Mean :-0.4708 Mean :0.2681178
## 3rd Qu.: 0.4607 3rd Qu.:0.4804344
## Max. : 1.3922 Max. :0.8402738
##
##
## $tables$Apolipoprotein_B
## $tables$Apolipoprotein_B$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.6047
##
## x y
## Min. :-10.7466 Min. :0.000115
## 1st Qu.: -8.1446 1st Qu.:0.015421
## Median : -5.5425 Median :0.082585
## Mean : -5.5425 Mean :0.095981
## 3rd Qu.: -2.9405 3rd Qu.:0.162206
## Max. : -0.3385 Max. :0.246168
##
## $tables$Apolipoprotein_B$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.4484
##
## x y
## Min. :-11.2822 Min. :8.789e-05
## 1st Qu.: -8.6635 1st Qu.:8.311e-03
## Median : -6.0448 Median :5.669e-02
## Mean : -6.0448 Mean :9.537e-02
## 3rd Qu.: -3.4261 3rd Qu.:1.985e-01
## Max. : -0.8073 Max. :2.741e-01
##
##
## $tables$Apolipoprotein_CI
## $tables$Apolipoprotein_CI$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1594
##
## x y
## Min. :-3.15171 Min. :0.0003857
## 1st Qu.:-2.34815 1st Qu.:0.0367762
## Median :-1.54458 Median :0.1791420
## Mean :-1.54458 Mean :0.3107990
## 3rd Qu.:-0.74102 3rd Qu.:0.5831359
## Max. : 0.06255 Max. :0.9619087
##
## $tables$Apolipoprotein_CI$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1045
##
## x y
## Min. :-3.63779 Min. :0.0002228
## 1st Qu.:-2.71856 1st Qu.:0.0149958
## Median :-1.79934 Median :0.0686617
## Mean :-1.79934 Mean :0.2716985
## 3rd Qu.:-0.88011 3rd Qu.:0.4137208
## Max. : 0.03912 Max. :1.1971584
##
##
## $tables$Apolipoprotein_CIII
## $tables$Apolipoprotein_CIII$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1455
##
## x y
## Min. :-3.9768 Min. :0.0004637
## 1st Qu.:-3.2303 1st Qu.:0.0770778
## Median :-2.4838 Median :0.1628703
## Mean :-2.4838 Mean :0.3345420
## 3rd Qu.:-1.7373 3rd Qu.:0.6348092
## Max. :-0.9907 Max. :1.0266980
##
## $tables$Apolipoprotein_CIII$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1236
##
## x y
## Min. :-4.0596 Min. :0.0001885
## 1st Qu.:-3.2615 1st Qu.:0.0384890
## Median :-2.4634 Median :0.1447592
## Mean :-2.4634 Mean :0.3129321
## 3rd Qu.:-1.6653 3rd Qu.:0.5925508
## Max. :-0.8672 Max. :1.0147825
##
##
## $tables$Apolipoprotein_D
## $tables$Apolipoprotein_D$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1309
##
## x y
## Min. :0.3494 Min. :0.0005334
## 1st Qu.:0.9282 1st Qu.:0.0740009
## Median :1.5070 Median :0.3576682
## Mean :1.5070 Mean :0.4314626
## 3rd Qu.:2.0859 3rd Qu.:0.8271840
## Max. :2.6647 Max. :1.0117737
##
## $tables$Apolipoprotein_D$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.09731
##
## x y
## Min. :0.1781 Min. :0.0002387
## 1st Qu.:0.7445 1st Qu.:0.0419120
## Median :1.3109 Median :0.3068511
## Mean :1.3109 Mean :0.4409405
## 3rd Qu.:1.8773 3rd Qu.:0.7832781
## Max. :2.4437 Max. :1.2360115
##
##
## $tables$Apolipoprotein_E
## $tables$Apolipoprotein_E$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.253
##
## x y
## Min. :-0.168 Min. :0.0002432
## 1st Qu.: 1.285 1st Qu.:0.0353311
## Median : 2.738 Median :0.0701827
## Mean : 2.738 Mean :0.1718667
## 3rd Qu.: 4.191 3rd Qu.:0.2943375
## Max. : 5.645 Max. :0.5678495
##
## $tables$Apolipoprotein_E$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.2394
##
## x y
## Min. :0.2702 Min. :0.0000969
## 1st Qu.:1.7432 1st Qu.:0.0092635
## Median :3.2163 Median :0.1031621
## Mean :3.2163 Mean :0.1695466
## 3rd Qu.:4.6894 3rd Qu.:0.3012086
## Max. :6.1624 Max. :0.5307705
##
##
## $tables$Apolipoprotein_H
## $tables$Apolipoprotein_H$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1461
##
## x y
## Min. :-1.5024 Min. :0.0004303
## 1st Qu.:-0.8516 1st Qu.:0.0522465
## Median :-0.2007 Median :0.2725912
## Mean :-0.2007 Mean :0.3837215
## 3rd Qu.: 0.4501 3rd Qu.:0.7312203
## Max. : 1.1010 Max. :0.9671531
##
## $tables$Apolipoprotein_H$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1182
##
## x y
## Min. :-2.5885 Min. :0.0000089
## 1st Qu.:-1.6210 1st Qu.:0.0062894
## Median :-0.6534 Median :0.0534648
## Mean :-0.6534 Mean :0.2581317
## 3rd Qu.: 0.3141 3rd Qu.:0.4463245
## Max. : 1.2817 Max. :1.1072897
##
##
## $tables$B_Lymphocyte_Chemoattractant_BL
## $tables$B_Lymphocyte_Chemoattractant_BL$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1786
##
## x y
## Min. :0.2631 Min. :0.0003441
## 1st Qu.:1.2047 1st Qu.:0.0345293
## Median :2.1462 Median :0.2156773
## Mean :2.1462 Mean :0.2652364
## 3rd Qu.:3.0878 3rd Qu.:0.4195428
## Max. :4.0294 Max. :0.7288469
##
## $tables$B_Lymphocyte_Chemoattractant_BL$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1635
##
## x y
## Min. :0.2412 Min. :0.0001417
## 1st Qu.:1.3095 1st Qu.:0.0158237
## Median :2.3778 Median :0.1518556
## Mean :2.3778 Mean :0.2337838
## 3rd Qu.:3.4460 3rd Qu.:0.4264705
## Max. :4.5143 Max. :0.7418276
##
##
## $tables$BMP_6
## $tables$BMP_6$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.09284
##
## x y
## Min. :-3.0397 Min. :0.0006656
## 1st Qu.:-2.4143 1st Qu.:0.0450255
## Median :-1.7889 Median :0.1862260
## Mean :-1.7889 Mean :0.3993483
## 3rd Qu.:-1.1635 3rd Qu.:0.6887474
## Max. :-0.5381 Max. :1.3771226
##
## $tables$BMP_6$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.09612
##
## x y
## Min. :-3.0495 Min. :0.0002407
## 1st Qu.:-2.4461 1st Qu.:0.0386422
## Median :-1.8426 Median :0.1397871
## Mean :-1.8426 Mean :0.4138639
## 3rd Qu.:-1.2391 3rd Qu.:0.9169796
## Max. :-0.6357 Max. :1.2649548
##
##
## $tables$Beta_2_Microglobulin
## $tables$Beta_2_Microglobulin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1171
##
## x y
## Min. :-0.8960 Min. :0.0005769
## 1st Qu.:-0.3551 1st Qu.:0.0655206
## Median : 0.1858 Median :0.2973803
## Mean : 0.1858 Mean :0.4617414
## 3rd Qu.: 0.7267 3rd Qu.:0.7195566
## Max. : 1.2675 Max. :1.4168425
##
## $tables$Beta_2_Microglobulin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08836
##
## x y
## Min. :-0.8098 Min. :0.000525
## 1st Qu.:-0.2928 1st Qu.:0.060180
## Median : 0.2243 Median :0.326347
## Mean : 0.2243 Mean :0.483040
## 3rd Qu.: 0.7413 3rd Qu.:0.854714
## Max. : 1.2583 Max. :1.480651
##
##
## $tables$Betacellulin
## $tables$Betacellulin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 4.15
##
## x y
## Min. :19.55 Min. :1.484e-05
## 1st Qu.:38.28 1st Qu.:1.812e-03
## Median :57.00 Median :1.039e-02
## Mean :57.00 Mean :1.334e-02
## 3rd Qu.:75.72 3rd Qu.:2.314e-02
## Max. :94.45 Max. :3.915e-02
##
## $tables$Betacellulin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 3.417
##
## x y
## Min. :-0.2524 Min. :6.770e-06
## 1st Qu.:22.8738 1st Qu.:5.621e-04
## Median :46.0000 Median :3.708e-03
## Mean :46.0000 Mean :1.080e-02
## 3rd Qu.:69.1262 3rd Qu.:2.258e-02
## Max. :92.2524 Max. :3.572e-02
##
##
## $tables$C_Reactive_Protein
## $tables$C_Reactive_Protein$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.434
##
## x y
## Min. :-9.483 Min. :0.0001476
## 1st Qu.:-7.632 1st Qu.:0.0195472
## Median :-5.781 Median :0.1183672
## Mean :-5.781 Mean :0.1349349
## 3rd Qu.:-3.930 3rd Qu.:0.2388263
## Max. :-2.079 Max. :0.3140977
##
## $tables$C_Reactive_Protein$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.3559
##
## x y
## Min. :-9.585 Min. :0.0001087
## 1st Qu.:-7.656 1st Qu.:0.0271837
## Median :-5.727 Median :0.1113366
## Mean :-5.727 Mean :0.1294817
## 3rd Qu.:-3.798 3rd Qu.:0.2303005
## Max. :-1.870 Max. :0.3027026
##
##
## $tables$CD40
## $tables$CD40$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.07961
##
## x y
## Min. :-2.0840 Min. :0.0007705
## 1st Qu.:-1.6785 1st Qu.:0.0782941
## Median :-1.2730 Median :0.3603564
## Mean :-1.2730 Mean :0.6158712
## 3rd Qu.:-0.8675 3rd Qu.:1.1334020
## Max. :-0.4620 Max. :1.8706943
##
## $tables$CD40$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.05743
##
## x y
## Min. :-2.0367 Min. :0.0004048
## 1st Qu.:-1.6213 1st Qu.:0.0555083
## Median :-1.2059 Median :0.2197493
## Mean :-1.2059 Mean :0.6012647
## 3rd Qu.:-0.7905 3rd Qu.:1.0463064
## Max. :-0.3752 Max. :2.2212000
##
##
## $tables$CD5L
## $tables$CD5L$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.172
##
## x y
## Min. :-1.6871 Min. :0.0004249
## 1st Qu.:-0.9073 1st Qu.:0.0658147
## Median :-0.1274 Median :0.2615887
## Mean :-0.1274 Mean :0.3202543
## 3rd Qu.: 0.6524 3rd Qu.:0.5485202
## Max. : 1.4322 Max. :0.8458993
##
## $tables$CD5L$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.133
##
## x y
## Min. :-1.63693 Min. :0.0002126
## 1st Qu.:-0.83715 1st Qu.:0.0421834
## Median :-0.03736 Median :0.1639564
## Mean :-0.03736 Mean :0.3122739
## 3rd Qu.: 0.76242 3rd Qu.:0.5645930
## Max. : 1.56221 Max. :0.9676547
##
##
## $tables$Calbindin
## $tables$Calbindin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 1.699
##
## x y
## Min. : 8.134 Min. :3.737e-05
## 1st Qu.:15.819 1st Qu.:4.415e-03
## Median :23.504 Median :2.338e-02
## Mean :23.504 Mean :3.250e-02
## 3rd Qu.:31.189 3rd Qu.:6.292e-02
## Max. :38.874 Max. :8.157e-02
##
## $tables$Calbindin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 1.104
##
## x y
## Min. : 7.65 Min. :2.227e-05
## 1st Qu.:14.87 1st Qu.:6.155e-03
## Median :22.09 Median :1.698e-02
## Mean :22.09 Mean :3.460e-02
## 3rd Qu.:29.31 3rd Qu.:6.257e-02
## Max. :36.52 Max. :1.083e-01
##
##
## $tables$Calcitonin
## $tables$Calcitonin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.3538
##
## x y
## Min. :-1.77474 Min. :0.000174
## 1st Qu.:-0.09275 1st Qu.:0.014686
## Median : 1.58923 Median :0.100639
## Mean : 1.58923 Mean :0.148483
## 3rd Qu.: 3.27122 3rd Qu.:0.265053
## Max. : 4.95321 Max. :0.421868
##
## $tables$Calcitonin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.2686
##
## x y
## Min. :-1.51917 Min. :0.0001728
## 1st Qu.:-0.02852 1st Qu.:0.0197102
## Median : 1.46212 Median :0.1069908
## Mean : 1.46212 Mean :0.1675440
## 3rd Qu.: 2.95276 3rd Qu.:0.3261255
## Max. : 4.44340 Max. :0.4276567
##
##
## $tables$CgA
## $tables$CgA$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 30.97
##
## x y
## Min. : 45.03 Min. :1.998e-06
## 1st Qu.:190.12 1st Qu.:1.972e-04
## Median :335.20 Median :1.250e-03
## Mean :335.20 Mean :1.721e-03
## 3rd Qu.:480.29 3rd Qu.:3.091e-03
## Max. :625.37 Max. :4.649e-03
##
## $tables$CgA$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 26.56
##
## x y
## Min. : 55.93 Min. :1.734e-06
## 1st Qu.:195.71 1st Qu.:3.219e-04
## Median :335.50 Median :1.212e-03
## Mean :335.50 Mean :1.787e-03
## 3rd Qu.:475.29 3rd Qu.:3.255e-03
## Max. :615.08 Max. :4.501e-03
##
##
## $tables$Clusterin_Apo_J
## $tables$Clusterin_Apo_J$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1155
##
## x y
## Min. :1.525 Min. :0.0005315
## 1st Qu.:2.127 1st Qu.:0.0219874
## Median :2.728 Median :0.1833987
## Mean :2.728 Mean :0.4154706
## 3rd Qu.:3.329 3rd Qu.:0.8299092
## Max. :3.930 Max. :1.2079520
##
## $tables$Clusterin_Apo_J$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.0788
##
## x y
## Min. :1.724 Min. :0.0002941
## 1st Qu.:2.248 1st Qu.:0.0431467
## Median :2.772 Median :0.2924763
## Mean :2.772 Mean :0.4765751
## 3rd Qu.:3.296 3rd Qu.:0.7761317
## Max. :3.820 Max. :1.5125773
##
##
## $tables$Complement_3
## $tables$Complement_3$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.9024
##
## x y
## Min. :-23.309 Min. :6.988e-05
## 1st Qu.:-19.196 1st Qu.:6.943e-03
## Median :-15.082 Median :4.538e-02
## Mean :-15.082 Mean :6.071e-02
## 3rd Qu.:-10.969 3rd Qu.:1.206e-01
## Max. : -6.855 Max. :1.459e-01
##
## $tables$Complement_3$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.7662
##
## x y
## Min. :-25.686 Min. :0.0000302
## 1st Qu.:-21.269 1st Qu.:0.0025761
## Median :-16.853 Median :0.0406163
## Mean :-16.853 Mean :0.0565461
## 3rd Qu.:-12.436 3rd Qu.:0.1164758
## Max. : -8.019 Max. :0.1469122
##
##
## $tables$Complement_Factor_H
## $tables$Complement_Factor_H$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.4561
##
## x y
## Min. :-0.6365 Min. :0.0001346
## 1st Qu.: 1.6432 1st Qu.:0.0102099
## Median : 3.9228 Median :0.0678452
## Mean : 3.9228 Mean :0.1095548
## 3rd Qu.: 6.2025 3rd Qu.:0.2233410
## Max. : 8.4821 Max. :0.2977210
##
## $tables$Complement_Factor_H$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.344
##
## x y
## Min. :-1.8708 Min. :0.0000674
## 1st Qu.: 0.7609 1st Qu.:0.0058335
## Median : 3.3926 Median :0.0287641
## Mean : 3.3926 Mean :0.0949018
## 3rd Qu.: 6.0243 3rd Qu.:0.1688771
## Max. : 8.6560 Max. :0.3764457
##
##
## $tables$Connective_Tissue_Growth_Factor
## $tables$Connective_Tissue_Growth_Factor$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.07808
##
## x y
## Min. :0.1022 Min. :0.0008626
## 1st Qu.:0.4484 1st Qu.:0.1084199
## Median :0.7946 Median :0.5583493
## Mean :0.7946 Mean :0.7213855
## 3rd Qu.:1.1408 3rd Qu.:1.3376449
## Max. :1.4870 Max. :1.8371337
##
## $tables$Connective_Tissue_Growth_Factor$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.06372
##
## x y
## Min. :-0.008838 Min. :0.0003648
## 1st Qu.: 0.393908 1st Qu.:0.0417652
## Median : 0.796654 Median :0.2897635
## Mean : 0.796654 Mean :0.6201238
## 3rd Qu.: 1.199401 3rd Qu.:1.2857304
## Max. : 1.602147 Max. :1.8383053
##
##
## $tables$Cortisol
## $tables$Cortisol$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 1.139
##
## x y
## Min. : 0.5829 Min. :5.461e-05
## 1st Qu.: 8.5415 1st Qu.:3.538e-03
## Median :16.5000 Median :9.519e-03
## Mean :16.5000 Mean :3.138e-02
## 3rd Qu.:24.4585 3rd Qu.:4.563e-02
## Max. :32.4171 Max. :1.343e-01
##
## $tables$Cortisol$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 1.095
##
## x y
## Min. :-3.185 Min. :6.347e-05
## 1st Qu.: 3.933 1st Qu.:5.143e-03
## Median :11.050 Median :1.131e-02
## Mean :11.050 Mean :3.509e-02
## 3rd Qu.:18.167 3rd Qu.:6.471e-02
## Max. :25.285 Max. :1.184e-01
##
##
## $tables$Creatine_Kinase_MB
## $tables$Creatine_Kinase_MB$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.02774
##
## x y
## Min. :-1.955 Min. :0.002214
## 1st Qu.:-1.810 1st Qu.:0.178384
## Median :-1.666 Median :0.808145
## Mean :-1.666 Mean :1.726062
## 3rd Qu.:-1.521 3rd Qu.:3.481272
## Max. :-1.376 Max. :5.174907
##
## $tables$Creatine_Kinase_MB$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.02971
##
## x y
## Min. :-1.961 Min. :0.000781
## 1st Qu.:-1.794 1st Qu.:0.209780
## Median :-1.628 Median :0.921181
## Mean :-1.628 Mean :1.498501
## 3rd Qu.:-1.461 3rd Qu.:2.713159
## Max. :-1.294 Max. :4.651081
##
##
## $tables$Cystatin_C
## $tables$Cystatin_C$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1319
##
## x y
## Min. :7.037 Min. :0.0004665
## 1st Qu.:7.712 1st Qu.:0.0414430
## Median :8.387 Median :0.2491322
## Mean :8.387 Mean :0.3699375
## 3rd Qu.:9.062 3rd Qu.:0.6303132
## Max. :9.737 Max. :1.2531383
##
## $tables$Cystatin_C$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1193
##
## x y
## Min. : 7.258 Min. :0.0003657
## 1st Qu.: 7.956 1st Qu.:0.0677482
## Median : 8.655 Median :0.2244309
## Mean : 8.655 Mean :0.3575216
## 3rd Qu.: 9.353 3rd Qu.:0.6489562
## Max. :10.052 Max. :0.9835258
##
##
## $tables$EGF_R
## $tables$EGF_R$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.08218
##
## x y
## Min. :-1.3578 Min. :0.0007491
## 1st Qu.:-0.9720 1st Qu.:0.0601772
## Median :-0.5862 Median :0.5146274
## Mean :-0.5862 Mean :0.6473375
## 3rd Qu.:-0.2004 3rd Qu.:1.2039048
## Max. : 0.1854 Max. :1.7136674
##
## $tables$EGF_R$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.07095
##
## x y
## Min. :-1.57421 Min. :0.0003305
## 1st Qu.:-1.16130 1st Qu.:0.0531718
## Median :-0.74840 Median :0.4016166
## Mean :-0.74840 Mean :0.6048693
## 3rd Qu.:-0.33550 3rd Qu.:1.1126174
## Max. : 0.07741 Max. :1.8041157
##
##
## $tables$EN_RAGE
## $tables$EN_RAGE$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2748
##
## x y
## Min. :-5.9404 Min. :0.0002424
## 1st Qu.:-4.5765 1st Qu.:0.0311518
## Median :-3.2127 Median :0.0839609
## Mean :-3.2127 Mean :0.1831140
## 3rd Qu.:-1.8488 3rd Qu.:0.3611623
## Max. :-0.4849 Max. :0.5223719
##
## $tables$EN_RAGE$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.2507
##
## x y
## Min. :-9.1295 Min. :0.0000000
## 1st Qu.:-6.7555 1st Qu.:0.0008704
## Median :-4.3815 Median :0.0119116
## Mean :-4.3815 Mean :0.1052045
## 3rd Qu.:-2.0076 3rd Qu.:0.1616271
## Max. : 0.3664 Max. :0.4756479
##
##
## $tables$ENA_78
## $tables$ENA_78$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.004593
##
## x y
## Min. :-1.412 Min. : 0.01568
## 1st Qu.:-1.392 1st Qu.: 1.83682
## Median :-1.372 Median :10.88628
## Mean :-1.372 Mean :12.47656
## 3rd Qu.:-1.352 3rd Qu.:20.69116
## Max. :-1.332 Max. :33.25567
##
## $tables$ENA_78$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.003951
##
## x y
## Min. :-1.417 Min. : 0.01243
## 1st Qu.:-1.395 1st Qu.: 1.90426
## Median :-1.372 Median : 6.31523
## Mean :-1.372 Mean :11.16134
## 3rd Qu.:-1.350 3rd Qu.:21.50399
## Max. :-1.328 Max. :30.63278
##
##
## $tables$Eotaxin_3
## $tables$Eotaxin_3$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 4.556
##
## x y
## Min. : 9.332 Min. :1.349e-05
## 1st Qu.: 37.166 1st Qu.:1.130e-03
## Median : 65.000 Median :5.515e-03
## Mean : 65.000 Mean :8.973e-03
## 3rd Qu.: 92.834 3rd Qu.:1.493e-02
## Max. :120.668 Max. :2.990e-02
##
## $tables$Eotaxin_3$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 4.684
##
## x y
## Min. : -7.052 Min. :4.944e-06
## 1st Qu.: 22.224 1st Qu.:4.102e-04
## Median : 51.500 Median :4.577e-03
## Mean : 51.500 Mean :8.531e-03
## 3rd Qu.: 80.776 3rd Qu.:1.858e-02
## Max. :110.052 Max. :2.366e-02
##
##
## $tables$FAS
## $tables$FAS$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.112
##
## x y
## Min. :-1.3860 Min. :0.0005531
## 1st Qu.:-0.8713 1st Qu.:0.0745803
## Median :-0.3567 Median :0.4054733
## Mean :-0.3567 Mean :0.4852608
## 3rd Qu.: 0.1580 3rd Qu.:0.8077443
## Max. : 0.6726 Max. :1.3569340
##
## $tables$FAS$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08631
##
## x y
## Min. :-1.7731 Min. :0.0002691
## 1st Qu.:-1.2412 1st Qu.:0.0229080
## Median :-0.7094 Median :0.3098072
## Mean :-0.7094 Mean :0.4696136
## 3rd Qu.:-0.1776 3rd Qu.:0.8475040
## Max. : 0.3542 Max. :1.4125738
##
##
## $tables$FSH_Follicle_Stimulation_Hormon
## $tables$FSH_Follicle_Stimulation_Hormon$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.168
##
## x y
## Min. :-2.619 Min. :0.0003674
## 1st Qu.:-1.814 1st Qu.:0.0438064
## Median :-1.009 Median :0.1809554
## Mean :-1.009 Mean :0.3102382
## 3rd Qu.:-0.204 3rd Qu.:0.5841814
## Max. : 0.601 Max. :0.8830041
##
## $tables$FSH_Follicle_Stimulation_Hormon$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1266
##
## x y
## Min. :-2.4537 Min. :0.0001923
## 1st Qu.:-1.7819 1st Qu.:0.0381824
## Median :-1.1101 Median :0.3304353
## Mean :-1.1101 Mean :0.3717625
## 3rd Qu.:-0.4383 3rd Qu.:0.7263199
## Max. : 0.2335 Max. :0.8705564
##
##
## $tables$Fas_Ligand
## $tables$Fas_Ligand$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.3972
##
## x y
## Min. :-0.9036 Min. :0.0001546
## 1st Qu.: 1.5284 1st Qu.:0.0075880
## Median : 3.9604 Median :0.0323851
## Mean : 3.9604 Mean :0.1026918
## 3rd Qu.: 6.3924 3rd Qu.:0.1767389
## Max. : 8.8244 Max. :0.3748798
##
## $tables$Fas_Ligand$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.3209
##
## x y
## Min. :-1.1164 Min. :0.0000723
## 1st Qu.: 0.8362 1st Qu.:0.0099329
## Median : 2.7888 Median :0.0733688
## Mean : 2.7888 Mean :0.1279086
## 3rd Qu.: 4.7414 3rd Qu.:0.2412691
## Max. : 6.6940 Max. :0.3647957
##
##
## $tables$Fatty_Acid_Binding_Protein
## $tables$Fatty_Acid_Binding_Protein$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.3218
##
## x y
## Min. :-1.1366 Min. :0.0001926
## 1st Qu.: 0.3152 1st Qu.:0.0230065
## Median : 1.7671 Median :0.1196474
## Mean : 1.7671 Mean :0.1720153
## 3rd Qu.: 3.2189 3rd Qu.:0.3275265
## Max. : 4.6708 Max. :0.4310323
##
## $tables$Fatty_Acid_Binding_Protein$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.2219
##
## x y
## Min. :-1.7098 Min. :0.0001076
## 1st Qu.:-0.3112 1st Qu.:0.0178852
## Median : 1.0873 Median :0.0845715
## Mean : 1.0873 Mean :0.1785805
## 3rd Qu.: 2.4859 3rd Qu.:0.3441102
## Max. : 3.8844 Max. :0.5063274
##
##
## $tables$Ferritin
## $tables$Ferritin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2804
##
## x y
## Min. :0.05701 Min. :0.0002189
## 1st Qu.:1.41138 1st Qu.:0.0221106
## Median :2.76576 Median :0.1363044
## Mean :2.76576 Mean :0.1843933
## 3rd Qu.:4.12014 3rd Qu.:0.3471055
## Max. :5.47452 Max. :0.4740304
##
## $tables$Ferritin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.2345
##
## x y
## Min. :-0.09576 Min. :0.0000997
## 1st Qu.: 1.26235 1st Qu.:0.0175701
## Median : 2.62046 Median :0.1094760
## Mean : 2.62046 Mean :0.1838968
## 3rd Qu.: 3.97858 3rd Qu.:0.3545390
## Max. : 5.33669 Max. :0.4921428
##
##
## $tables$Fetuin_A
## $tables$Fetuin_A$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1072
##
## x y
## Min. :0.2090 Min. :0.0005753
## 1st Qu.:0.7837 1st Qu.:0.0670717
## Median :1.3583 Median :0.3001584
## Mean :1.3583 Mean :0.4345968
## 3rd Qu.:1.9330 3rd Qu.:0.6861384
## Max. :2.5077 Max. :1.3348011
##
## $tables$Fetuin_A$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1175
##
## x y
## Min. :0.1176 Min. :0.0002762
## 1st Qu.:0.7391 1st Qu.:0.0573634
## Median :1.3606 Median :0.3241058
## Mean :1.3606 Mean :0.4018497
## 3rd Qu.:1.9821 3rd Qu.:0.7113145
## Max. :2.6037 Max. :1.0642227
##
##
## $tables$Fibrinogen
## $tables$Fibrinogen$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2039
##
## x y
## Min. :-9.352 Min. :0.0003021
## 1st Qu.:-8.340 1st Qu.:0.0297846
## Median :-7.327 Median :0.1178889
## Mean :-7.327 Mean :0.2466951
## 3rd Qu.:-6.315 3rd Qu.:0.4610479
## Max. :-5.303 Max. :0.7651905
##
## $tables$Fibrinogen$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1616
##
## x y
## Min. :-9.359 Min. :0.0001784
## 1st Qu.:-8.359 1st Qu.:0.0386625
## Median :-7.358 Median :0.1269579
## Mean :-7.358 Mean :0.2497188
## 3rd Qu.:-6.358 3rd Qu.:0.4814026
## Max. :-5.358 Max. :0.7053784
##
##
## $tables$GRO_alpha
## $tables$GRO_alpha$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.01539
##
## x y
## Min. :1.263 Min. : 0.004096
## 1st Qu.:1.332 1st Qu.: 0.543326
## Median :1.402 Median : 2.722113
## Mean :1.402 Mean : 3.594376
## 3rd Qu.:1.471 3rd Qu.: 6.180870
## Max. :1.541 Max. :10.701419
##
## $tables$GRO_alpha$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.01113
##
## x y
## Min. :1.238 Min. : 0.002974
## 1st Qu.:1.300 1st Qu.: 0.969942
## Median :1.363 Median : 3.052944
## Mean :1.363 Mean : 3.998520
## 3rd Qu.:1.425 3rd Qu.: 7.206995
## Max. :1.488 Max. :10.080751
##
##
## $tables$Gamma_Interferon_induced_Monokin
## $tables$Gamma_Interferon_induced_Monokin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.03894
##
## x y
## Min. :2.497 Min. :0.001656
## 1st Qu.:2.669 1st Qu.:0.177821
## Median :2.840 Median :1.260277
## Mean :2.840 Mean :1.458628
## 3rd Qu.:3.011 3rd Qu.:2.703014
## Max. :3.182 Max. :3.513056
##
## $tables$Gamma_Interferon_induced_Monokin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.03562
##
## x y
## Min. :2.286 Min. :0.000651
## 1st Qu.:2.496 1st Qu.:0.054717
## Median :2.707 Median :0.800847
## Mean :2.707 Mean :1.189120
## 3rd Qu.:2.917 3rd Qu.:2.390646
## Max. :3.127 Max. :3.380559
##
##
## $tables$Glutathione_S_Transferase_alpha
## $tables$Glutathione_S_Transferase_alpha$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.04318
##
## x y
## Min. :0.5788 Min. :0.001428
## 1st Qu.:0.7883 1st Qu.:0.165853
## Median :0.9978 Median :0.940288
## Mean :0.9978 Mean :1.192302
## 3rd Qu.:1.2072 3rd Qu.:2.090001
## Max. :1.4167 Max. :3.409316
##
## $tables$Glutathione_S_Transferase_alpha$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.05304
##
## x y
## Min. :0.3647 Min. :0.0004962
## 1st Qu.:0.6427 1st Qu.:0.1125937
## Median :0.9207 Median :0.8441616
## Mean :0.9207 Mean :0.8983864
## 3rd Qu.:1.1987 3rd Qu.:1.5789411
## Max. :1.4767 Max. :2.3143650
##
##
## $tables$HB_EGF
## $tables$HB_EGF$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.5734
##
## x y
## Min. : 2.304 Min. :0.00013
## 1st Qu.: 4.704 1st Qu.:0.01763
## Median : 7.105 Median :0.09498
## Mean : 7.105 Mean :0.10404
## 3rd Qu.: 9.505 3rd Qu.:0.19944
## Max. :11.906 Max. :0.21642
##
## $tables$HB_EGF$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.4589
##
## x y
## Min. : 0.7264 Min. :5.061e-05
## 1st Qu.: 3.5627 1st Qu.:7.274e-03
## Median : 6.3991 Median :4.540e-02
## Mean : 6.3991 Mean :8.806e-02
## 3rd Qu.: 9.2354 3rd Qu.:1.655e-01
## Max. :12.0717 Max. :2.739e-01
##
##
## $tables$HCC_4
## $tables$HCC_4$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1258
##
## x y
## Min. :-4.513 Min. :0.0004882
## 1st Qu.:-3.842 1st Qu.:0.0264179
## Median :-3.171 Median :0.1776489
## Mean :-3.171 Mean :0.3723693
## 3rd Qu.:-2.501 3rd Qu.:0.7273183
## Max. :-1.830 Max. :1.1556610
##
## $tables$HCC_4$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1143
##
## x y
## Min. :-4.853 Min. :0.0002034
## 1st Qu.:-4.084 1st Qu.:0.0155743
## Median :-3.315 Median :0.0900680
## Mean :-3.315 Mean :0.3248168
## 3rd Qu.:-2.546 3rd Qu.:0.6484402
## Max. :-1.777 Max. :1.1135097
##
##
## $tables$Hepatocyte_Growth_Factor_HGF
## $tables$Hepatocyte_Growth_Factor_HGF$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1039
##
## x y
## Min. :-0.6264 Min. :0.0006696
## 1st Qu.:-0.1730 1st Qu.:0.0920590
## Median : 0.2804 Median :0.4441200
## Mean : 0.2804 Mean :0.5508391
## 3rd Qu.: 0.7338 3rd Qu.:0.9890205
## Max. : 1.1871 Max. :1.3570206
##
## $tables$Hepatocyte_Growth_Factor_HGF$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08115
##
## x y
## Min. :-0.8783 Min. :0.000286
## 1st Qu.:-0.3790 1st Qu.:0.043393
## Median : 0.1203 Median :0.379510
## Mean : 0.1203 Mean :0.500188
## 3rd Qu.: 0.6196 3rd Qu.:0.892127
## Max. : 1.1189 Max. :1.419948
##
##
## $tables$I_309
## $tables$I_309$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.149
##
## x y
## Min. :1.568 Min. :0.0004133
## 1st Qu.:2.323 1st Qu.:0.0398974
## Median :3.079 Median :0.1734239
## Mean :3.079 Mean :0.3305474
## 3rd Qu.:3.835 3rd Qu.:0.6190918
## Max. :4.590 Max. :1.0464055
##
## $tables$I_309$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1101
##
## x y
## Min. :1.428 Min. :0.0002591
## 1st Qu.:2.105 1st Qu.:0.0400834
## Median :2.782 Median :0.1469666
## Mean :2.782 Mean :0.3687393
## 3rd Qu.:3.460 3rd Qu.:0.7281925
## Max. :4.137 Max. :1.1793030
##
##
## $tables$ICAM_1
## $tables$ICAM_1$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1211
##
## x y
## Min. :-1.896401 Min. :0.0005117
## 1st Qu.:-1.262346 1st Qu.:0.0588241
## Median :-0.628291 Median :0.2161999
## Mean :-0.628291 Mean :0.3938882
## 3rd Qu.: 0.005764 3rd Qu.:0.6731638
## Max. : 0.639819 Max. :1.2102214
##
## $tables$ICAM_1$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1011
##
## x y
## Min. :-1.6327 Min. :0.0002293
## 1st Qu.:-1.0195 1st Qu.:0.0168578
## Median :-0.4062 Median :0.2626844
## Mean :-0.4062 Mean :0.4072439
## 3rd Qu.: 0.2071 3rd Qu.:0.7472092
## Max. : 0.8203 Max. :1.2308291
##
##
## $tables$IGF_BP_2
## $tables$IGF_BP_2$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.08533
##
## x y
## Min. :4.407 Min. :0.0007209
## 1st Qu.:4.857 1st Qu.:0.0626809
## Median :5.306 Median :0.1652539
## Mean :5.306 Mean :0.5560455
## 3rd Qu.:5.755 3rd Qu.:1.1496296
## Max. :6.204 Max. :1.6631547
##
## $tables$IGF_BP_2$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.05889
##
## x y
## Min. :4.458 Min. :0.0004181
## 1st Qu.:4.833 1st Qu.:0.0414194
## Median :5.208 Median :0.3289988
## Mean :5.208 Mean :0.6662805
## 3rd Qu.:5.583 3rd Qu.:1.2907218
## Max. :5.957 Max. :1.9869876
##
##
## $tables$IL_11
## $tables$IL_11$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.6032
##
## x y
## Min. :-0.05488 Min. :0.0001116
## 1st Qu.: 2.34389 1st Qu.:0.0173141
## Median : 4.74267 Median :0.1182110
## Mean : 4.74267 Mean :0.1041116
## 3rd Qu.: 7.14144 3rd Qu.:0.1744307
## Max. : 9.54022 Max. :0.2217994
##
## $tables$IL_11$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.439
##
## x y
## Min. :0.7144 Min. :5.524e-05
## 1st Qu.:2.9878 1st Qu.:1.992e-02
## Median :5.2611 Median :8.475e-02
## Mean :5.2611 Mean :1.099e-01
## 3rd Qu.:7.5344 3rd Qu.:1.965e-01
## Max. :9.8078 Max. :2.837e-01
##
##
## $tables$IL_13
## $tables$IL_13$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.002242
##
## x y
## Min. :1.258 Min. : 0.02755
## 1st Qu.:1.272 1st Qu.: 5.04144
## Median :1.286 Median :11.45446
## Mean :1.286 Mean :17.66084
## 3rd Qu.:1.300 3rd Qu.:32.61748
## Max. :1.314 Max. :46.60025
##
## $tables$IL_13$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.003487
##
## x y
## Min. :1.249 Min. : 0.00675
## 1st Qu.:1.269 1st Qu.: 1.21607
## Median :1.290 Median : 6.44502
## Mean :1.290 Mean :12.11136
## 3rd Qu.:1.310 3rd Qu.:24.13952
## Max. :1.331 Max. :35.44771
##
##
## $tables$IL_16
## $tables$IL_16$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2471
##
## x y
## Min. :0.7188 Min. :0.0002583
## 1st Qu.:1.8146 1st Qu.:0.0295564
## Median :2.9104 Median :0.1767049
## Mean :2.9104 Mean :0.2279070
## 3rd Qu.:4.0062 3rd Qu.:0.4144416
## Max. :5.1020 Max. :0.5606021
##
## $tables$IL_16$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.181
##
## x y
## Min. :0.6436 Min. :0.0001303
## 1st Qu.:1.8526 1st Qu.:0.0285828
## Median :3.0616 Median :0.1210115
## Mean :3.0616 Mean :0.2065752
## 3rd Qu.:4.2706 3rd Qu.:0.3508192
## Max. :5.4797 Max. :0.6960411
##
##
## $tables$IL_17E
## $tables$IL_17E$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.3349
##
## x y
## Min. :0.7917 Min. :0.000185
## 1st Qu.:2.6386 1st Qu.:0.030212
## Median :4.4855 Median :0.057871
## Mean :4.4855 Mean :0.135222
## 3rd Qu.:6.3324 3rd Qu.:0.275862
## Max. :8.1793 Max. :0.398278
##
## $tables$IL_17E$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.3705
##
## x y
## Min. :-0.05917 Min. :6.355e-05
## 1st Qu.: 2.47145 1st Qu.:1.477e-02
## Median : 5.00207 Median :5.721e-02
## Mean : 5.00207 Mean :9.869e-02
## 3rd Qu.: 7.53269 3rd Qu.:1.705e-01
## Max. :10.06331 Max. :2.947e-01
##
##
## $tables$IL_1alpha
## $tables$IL_1alpha$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1388
##
## x y
## Min. :-8.597 Min. :0.0004414
## 1st Qu.:-7.832 1st Qu.:0.0345549
## Median :-7.066 Median :0.1790469
## Mean :-7.066 Mean :0.3263385
## 3rd Qu.:-6.301 3rd Qu.:0.6214391
## Max. :-5.536 Max. :0.9966209
##
## $tables$IL_1alpha$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1167
##
## x y
## Min. :-8.867 Min. :0.0001999
## 1st Qu.:-8.206 1st Qu.:0.0415305
## Median :-7.544 Median :0.2441260
## Mean :-7.544 Mean :0.3775143
## 3rd Qu.:-6.883 3rd Qu.:0.7631243
## Max. :-6.221 Max. :1.0542670
##
##
## $tables$IL_3
## $tables$IL_3$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1651
##
## x y
## Min. :-5.968 Min. :0.0003733
## 1st Qu.:-5.051 1st Qu.:0.0310725
## Median :-4.135 Median :0.1619700
## Mean :-4.135 Mean :0.2724682
## 3rd Qu.:-3.218 3rd Qu.:0.4907103
## Max. :-2.302 Max. :0.8265465
##
## $tables$IL_3$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.144
##
## x y
## Min. :-6.347 Min. :0.0001608
## 1st Qu.:-5.265 1st Qu.:0.0094314
## Median :-4.184 Median :0.0893512
## Mean :-4.184 Mean :0.2309658
## 3rd Qu.:-3.103 3rd Qu.:0.4009027
## Max. :-2.021 Max. :0.9033615
##
##
## $tables$IL_4
## $tables$IL_4$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.172
##
## x y
## Min. :0.01472 Min. :0.000391
## 1st Qu.:0.90115 1st Qu.:0.072016
## Median :1.78757 Median :0.134568
## Mean :1.78757 Mean :0.281739
## 3rd Qu.:2.67400 3rd Qu.:0.525905
## Max. :3.56043 Max. :0.824690
##
## $tables$IL_4$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1575
##
## x y
## Min. :0.1154 Min. :0.0003506
## 1st Qu.:0.9536 1st Qu.:0.0534137
## Median :1.7918 Median :0.2215265
## Mean :1.7918 Mean :0.2979560
## 3rd Qu.:2.6300 3rd Qu.:0.5431015
## Max. :3.4681 Max. :0.7481607
##
##
## $tables$IL_5
## $tables$IL_5$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1651
##
## x y
## Min. :-1.54514 Min. :0.0003731
## 1st Qu.:-0.74424 1st Qu.:0.0354730
## Median : 0.05666 Median :0.2059910
## Mean : 0.05666 Mean :0.3118307
## 3rd Qu.: 0.85757 3rd Qu.:0.5667951
## Max. : 1.65847 Max. :0.8659126
##
## $tables$IL_5$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1448
##
## x y
## Min. :-1.8615 Min. :0.0001599
## 1st Qu.:-0.8011 1st Qu.:0.0108494
## Median : 0.2594 Median :0.0628554
## Mean : 0.2594 Mean :0.2355126
## 3rd Qu.: 1.3199 3rd Qu.:0.4398579
## Max. : 2.3803 Max. :0.8382137
##
##
## $tables$IL_6
## $tables$IL_6$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1704
##
## x y
## Min. :-1.9629 Min. :0.0003617
## 1st Qu.:-0.8909 1st Qu.:0.0433293
## Median : 0.1811 Median :0.1087926
## Mean : 0.1811 Mean :0.2329778
## 3rd Qu.: 1.2530 3rd Qu.:0.3663787
## Max. : 2.3250 Max. :0.9425837
##
## $tables$IL_6$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1184
##
## x y
## Min. :-1.8894 Min. :0.0002635
## 1st Qu.:-1.0009 1st Qu.:0.0583595
## Median :-0.1124 Median :0.1535668
## Mean :-0.1124 Mean :0.2810808
## 3rd Qu.: 0.7762 3rd Qu.:0.4338186
## Max. : 1.6647 Max. :0.9333781
##
##
## $tables$IL_6_Receptor
## $tables$IL_6_Receptor$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1073
##
## x y
## Min. :-0.99740 Min. :0.0005748
## 1st Qu.:-0.45986 1st Qu.:0.0730745
## Median : 0.07769 Median :0.2995523
## Mean : 0.07769 Mean :0.4645986
## 3rd Qu.: 0.61523 3rd Qu.:0.8863563
## Max. : 1.15278 Max. :1.2160557
##
## $tables$IL_6_Receptor$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1004
##
## x y
## Min. :-0.94324 Min. :0.0005696
## 1st Qu.:-0.45521 1st Qu.:0.1064961
## Median : 0.03281 Median :0.5015336
## Mean : 0.03281 Mean :0.5117502
## 3rd Qu.: 0.52084 3rd Qu.:0.9280242
## Max. : 1.00887 Max. :1.0834743
##
##
## $tables$IL_7
## $tables$IL_7$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.4111
##
## x y
## Min. :-0.6736 Min. :0.0004602
## 1st Qu.: 0.9327 1st Qu.:0.0443270
## Median : 2.5390 Median :0.1449666
## Mean : 2.5390 Mean :0.1554575
## 3rd Qu.: 4.1454 3rd Qu.:0.2584508
## Max. : 5.7517 Max. :0.3419315
##
## $tables$IL_7$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.3193
##
## x y
## Min. :-0.273 Min. :0.0000741
## 1st Qu.: 1.461 1st Qu.:0.0171206
## Median : 3.195 Median :0.0966736
## Mean : 3.195 Mean :0.1440193
## 3rd Qu.: 4.929 3rd Qu.:0.2853746
## Max. : 6.664 Max. :0.3483485
##
##
## $tables$IL_8
## $tables$IL_8$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.01351
##
## x y
## Min. :1.567 Min. : 0.004558
## 1st Qu.:1.637 1st Qu.: 0.483418
## Median :1.707 Median : 1.880662
## Mean :1.707 Mean : 3.568575
## 3rd Qu.:1.777 3rd Qu.: 6.309919
## Max. :1.847 Max. :10.740981
##
## $tables$IL_8$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.0109
##
## x y
## Min. :1.541 Min. : 0.002128
## 1st Qu.:1.607 1st Qu.: 0.240305
## Median :1.674 Median : 2.006761
## Mean :1.674 Mean : 3.765004
## 3rd Qu.:1.740 3rd Qu.: 7.246188
## Max. :1.806 Max. :11.193910
##
##
## $tables$IP_10_Inducible_Protein_10
## $tables$IP_10_Inducible_Protein_10$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1863
##
## x y
## Min. :4.142 Min. :0.0004224
## 1st Qu.:4.966 1st Qu.:0.0475977
## Median :5.790 Median :0.2379051
## Mean :5.790 Mean :0.3030403
## 3rd Qu.:6.614 3rd Qu.:0.5362121
## Max. :7.438 Max. :0.7593750
##
## $tables$IP_10_Inducible_Protein_10$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1494
##
## x y
## Min. :3.869 Min. :0.0001661
## 1st Qu.:4.889 1st Qu.:0.0185911
## Median :5.909 Median :0.0913981
## Mean :5.909 Mean :0.2448436
## 3rd Qu.:6.929 3rd Qu.:0.5134322
## Max. :7.949 Max. :0.7722094
##
##
## $tables$IgA
## $tables$IgA$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2723
##
## x y
## Min. :-8.616 Min. :0.0002259
## 1st Qu.:-7.325 1st Qu.:0.0189179
## Median :-6.034 Median :0.1317102
## Mean :-6.034 Mean :0.1934408
## 3rd Qu.:-4.743 3rd Qu.:0.3951605
## Max. :-3.452 Max. :0.5023544
##
## $tables$IgA$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.2182
##
## x y
## Min. :-11.174 Min. :0.00000
## 1st Qu.: -9.267 1st Qu.:0.00134
## Median : -7.360 Median :0.01968
## Mean : -7.360 Mean :0.13095
## 3rd Qu.: -5.452 3rd Qu.:0.24182
## Max. : -3.545 Max. :0.56049
##
##
## $tables$Insulin
## $tables$Insulin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1036
##
## x y
## Min. :-2.4800 Min. :0.0005948
## 1st Qu.:-1.8574 1st Qu.:0.0556733
## Median :-1.2347 Median :0.3265261
## Mean :-1.2347 Mean :0.4011236
## 3rd Qu.:-0.6121 3rd Qu.:0.5628190
## Max. : 0.0105 Max. :1.2943572
##
## $tables$Insulin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08799
##
## x y
## Min. :-2.4331 Min. :0.0002829
## 1st Qu.:-1.7985 1st Qu.:0.0596625
## Median :-1.1639 Median :0.1893760
## Mean :-1.1639 Mean :0.3935434
## 3rd Qu.:-0.5293 3rd Qu.:0.6691906
## Max. : 0.1053 Max. :1.4077957
##
##
## $tables$Kidney_Injury_Molecule_1_KIM_1
## $tables$Kidney_Injury_Molecule_1_KIM_1$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.00858
##
## x y
## Min. :-1.257 Min. : 0.007629
## 1st Qu.:-1.218 1st Qu.: 0.914691
## Median :-1.178 Median : 3.944989
## Mean :-1.178 Mean : 6.276158
## 3rd Qu.:-1.138 3rd Qu.:11.647289
## Max. :-1.098 Max. :16.903643
##
## $tables$Kidney_Injury_Molecule_1_KIM_1$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.009108
##
## x y
## Min. :-1.283 Min. : 0.003862
## 1st Qu.:-1.232 1st Qu.: 0.594449
## Median :-1.180 Median : 2.997486
## Mean :-1.180 Mean : 4.861499
## 3rd Qu.:-1.129 3rd Qu.:10.118196
## Max. :-1.077 Max. :12.463212
##
##
## $tables$LOX_1
## $tables$LOX_1$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1414
##
## x y
## Min. :-0.1617 Min. :0.0005145
## 1st Qu.: 0.5077 1st Qu.:0.0658713
## Median : 1.1771 Median :0.2907096
## Mean : 1.1771 Mean :0.3730766
## 3rd Qu.: 1.8465 3rd Qu.:0.6069735
## Max. : 2.5160 Max. :1.0706141
##
## $tables$LOX_1$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1163
##
## x y
## Min. :-0.3488 Min. :0.0001992
## 1st Qu.: 0.3936 1st Qu.:0.0209012
## Median : 1.1361 Median :0.1974815
## Mean : 1.1361 Mean :0.3363988
## 3rd Qu.: 1.8785 3rd Qu.:0.5653404
## Max. : 2.6209 Max. :1.0575822
##
##
## $tables$Leptin
## $tables$Leptin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1027
##
## x y
## Min. :-2.3820 Min. :0.0005996
## 1st Qu.:-1.8724 1st Qu.:0.0348426
## Median :-1.3627 Median :0.3295843
## Mean :-1.3627 Mean :0.4900239
## 3rd Qu.:-0.8531 3rd Qu.:0.9552378
## Max. :-0.3435 Max. :1.4108164
##
## $tables$Leptin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08537
##
## x y
## Min. :-2.4030 Min. :0.0002711
## 1st Qu.:-1.8933 1st Qu.:0.0265899
## Median :-1.3837 Median :0.2662542
## Mean :-1.3837 Mean :0.4900776
## 3rd Qu.:-0.8741 3rd Qu.:1.0136124
## Max. :-0.3645 Max. :1.3471554
##
##
## $tables$Lipoprotein_a
## $tables$Lipoprotein_a$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.4463
##
## x y
## Min. :-7.50460 Min. :0.0001383
## 1st Qu.:-5.64033 1st Qu.:0.0177876
## Median :-3.77606 Median :0.1234638
## Mean :-3.77606 Mean :0.1339599
## 3rd Qu.:-1.91178 3rd Qu.:0.2473009
## Max. :-0.04751 Max. :0.3012031
##
## $tables$Lipoprotein_a$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.3384
##
## x y
## Min. :-7.828 Min. :0.0000713
## 1st Qu.:-5.974 1st Qu.:0.0130412
## Median :-4.120 Median :0.1009659
## Mean :-4.120 Mean :0.1347189
## 3rd Qu.:-2.266 3rd Qu.:0.2400472
## Max. :-0.412 Max. :0.3529706
##
##
## $tables$MCP_1
## $tables$MCP_1$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.08204
##
## x y
## Min. :5.580 Min. :0.0007486
## 1st Qu.:6.023 1st Qu.:0.0763868
## Median :6.466 Median :0.3347607
## Mean :6.466 Mean :0.5634995
## 3rd Qu.:6.910 3rd Qu.:0.9844137
## Max. :7.353 Max. :1.6611478
##
## $tables$MCP_1$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08365
##
## x y
## Min. :5.575 Min. :0.0002859
## 1st Qu.:6.051 1st Qu.:0.0504493
## Median :6.528 Median :0.3500525
## Mean :6.528 Mean :0.5242114
## 3rd Qu.:7.004 3rd Qu.:1.0140560
## Max. :7.481 Max. :1.4070201
##
##
## $tables$MCP_2
## $tables$MCP_2$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.258
##
## x y
## Min. :-0.3735 Min. :0.0002384
## 1st Qu.: 0.9194 1st Qu.:0.0271061
## Median : 2.2122 Median :0.0880462
## Mean : 2.2122 Mean :0.1931765
## 3rd Qu.: 3.5050 3rd Qu.:0.3591884
## Max. : 4.7978 Max. :0.6187589
##
## $tables$MCP_2$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1411
##
## x y
## Min. :-0.02269 Min. :0.0001648
## 1st Qu.: 1.09474 1st Qu.:0.0145562
## Median : 2.21217 Median :0.1158586
## Mean : 2.21217 Mean :0.2234860
## 3rd Qu.: 3.32960 3rd Qu.:0.3215262
## Max. : 4.44703 Max. :0.8589345
##
##
## $tables$MIF
## $tables$MIF$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1116
##
## x y
## Min. :-2.7318 Min. :0.000552
## 1st Qu.:-2.1761 1st Qu.:0.050600
## Median :-1.6204 Median :0.186149
## Mean :-1.6204 Mean :0.449453
## 3rd Qu.:-1.0648 3rd Qu.:0.997236
## Max. :-0.5091 Max. :1.177368
##
## $tables$MIF$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.09496
##
## x y
## Min. :-3.1322 Min. :0.000243
## 1st Qu.:-2.5133 1st Qu.:0.032731
## Median :-1.8945 Median :0.273283
## Mean :-1.8945 Mean :0.403567
## 3rd Qu.:-1.2756 3rd Qu.:0.735915
## Max. :-0.6567 Max. :1.257071
##
##
## $tables$MIP_1alpha
## $tables$MIP_1alpha$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2821
##
## x y
## Min. :1.541 Min. :0.0002261
## 1st Qu.:2.893 1st Qu.:0.0356451
## Median :4.244 Median :0.1367257
## Mean :4.244 Mean :0.1848367
## 3rd Qu.:5.595 3rd Qu.:0.3003292
## Max. :6.946 Max. :0.4964654
##
## $tables$MIP_1alpha$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.3311
##
## x y
## Min. :-0.05885 Min. :0.000070
## 1st Qu.: 1.90320 1st Qu.:0.007126
## Median : 3.86525 Median :0.075604
## Mean : 3.86525 Mean :0.127292
## 3rd Qu.: 5.82730 3rd Qu.:0.239002
## Max. : 7.78935 Max. :0.377147
##
##
## $tables$MIP_1beta
## $tables$MIP_1beta$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1583
##
## x y
## Min. :1.499 Min. :0.0004224
## 1st Qu.:2.245 1st Qu.:0.0515232
## Median :2.991 Median :0.1745231
## Mean :2.991 Mean :0.3349146
## 3rd Qu.:3.736 3rd Qu.:0.6736164
## Max. :4.482 Max. :0.9448572
##
## $tables$MIP_1beta$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1009
##
## x y
## Min. :1.643 Min. :0.0002533
## 1st Qu.:2.243 1st Qu.:0.0796220
## Median :2.842 Median :0.3248102
## Mean :2.842 Mean :0.4167598
## 3rd Qu.:3.441 3rd Qu.:0.6663666
## Max. :4.040 Max. :1.2441607
##
##
## $tables$MMP_2
## $tables$MMP_2$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2979
##
## x y
## Min. :-0.1107 Min. :0.0002086
## 1st Qu.: 1.4801 1st Qu.:0.0257316
## Median : 3.0710 Median :0.0677385
## Mean : 3.0710 Mean :0.1569916
## 3rd Qu.: 4.6618 3rd Qu.:0.3049826
## Max. : 6.2526 Max. :0.4970197
##
## $tables$MMP_2$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.2855
##
## x y
## Min. :-0.7584 Min. :0.0000814
## 1st Qu.: 0.8574 1st Qu.:0.0232488
## Median : 2.4732 Median :0.1038737
## Mean : 2.4732 Mean :0.1545649
## 3rd Qu.: 4.0890 3rd Qu.:0.2934098
## Max. : 5.7048 Max. :0.4103395
##
##
## $tables$MMP_3
## $tables$MMP_3$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1763
##
## x y
## Min. :-4.345539 Min. :0.0003502
## 1st Qu.:-3.258856 1st Qu.:0.0319722
## Median :-2.172173 Median :0.1427370
## Mean :-2.172173 Mean :0.2298252
## 3rd Qu.:-1.085490 3rd Qu.:0.3267422
## Max. : 0.001193 Max. :0.7715074
##
## $tables$MMP_3$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1614
##
## x y
## Min. :-4.9069 Min. :0.000149
## 1st Qu.:-3.8440 1st Qu.:0.020219
## Median :-2.7811 Median :0.119434
## Mean :-2.7811 Mean :0.234972
## 3rd Qu.:-1.7182 3rd Qu.:0.408401
## Max. :-0.6553 Max. :0.755037
##
##
## $tables$MMP10
## $tables$MMP10$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1366
##
## x y
## Min. :-5.343 Min. :0.0004514
## 1st Qu.:-4.457 1st Qu.:0.0326836
## Median :-3.570 Median :0.1318017
## Mean :-3.570 Mean :0.2817411
## 3rd Qu.:-2.684 3rd Qu.:0.4412923
## Max. :-1.798 Max. :1.1138765
##
## $tables$MMP10$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1146
##
## x y
## Min. :-5.011 Min. :0.0002398
## 1st Qu.:-4.313 1st Qu.:0.0639944
## Median :-3.615 Median :0.1885299
## Mean :-3.615 Mean :0.3579394
## 3rd Qu.:-2.918 3rd Qu.:0.7262746
## Max. :-2.220 Max. :0.9703164
##
##
## $tables$MMP7
## $tables$MMP7$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.4298
##
## x y
## Min. :-7.8961 Min. :0.0001895
## 1st Qu.:-5.7017 1st Qu.:0.0296584
## Median :-3.5072 Median :0.0703880
## Mean :-3.5072 Mean :0.1138034
## 3rd Qu.:-1.3127 3rd Qu.:0.2120591
## Max. : 0.8818 Max. :0.3286257
##
## $tables$MMP7$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.4545
##
## x y
## Min. :-9.761 Min. :5.093e-05
## 1st Qu.:-7.035 1st Qu.:7.612e-03
## Median :-4.310 Median :7.742e-02
## Mean :-4.310 Mean :9.163e-02
## 3rd Qu.:-1.584 3rd Qu.:1.514e-01
## Max. : 1.141 Max. :2.709e-01
##
##
## $tables$Myoglobin
## $tables$Myoglobin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2835
##
## x y
## Min. :-4.0204 Min. :0.0002171
## 1st Qu.:-2.3811 1st Qu.:0.0147377
## Median :-0.7418 Median :0.0571056
## Mean :-0.7418 Mean :0.1523496
## 3rd Qu.: 0.8975 3rd Qu.:0.2853323
## Max. : 2.5368 Max. :0.5057002
##
## $tables$Myoglobin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.3043
##
## x y
## Min. :-4.0364 Min. :0.0001422
## 1st Qu.:-2.3554 1st Qu.:0.0246486
## Median :-0.6743 Median :0.0900034
## Mean :-0.6743 Mean :0.1485664
## 3rd Qu.: 1.0068 3rd Qu.:0.2873699
## Max. : 2.6878 Max. :0.4050752
##
##
## $tables$NT_proBNP
## $tables$NT_proBNP$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1278
##
## x y
## Min. :3.488 Min. :0.0004842
## 1st Qu.:4.183 1st Qu.:0.0474230
## Median :4.879 Median :0.1647061
## Mean :4.879 Mean :0.3590951
## 3rd Qu.:5.574 3rd Qu.:0.6212857
## Max. :6.270 Max. :1.2360329
##
## $tables$NT_proBNP$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.09443
##
## x y
## Min. :2.895 Min. :0.0002468
## 1st Qu.:3.609 1st Qu.:0.0258146
## Median :4.323 Median :0.1532264
## Mean :4.323 Mean :0.3497230
## 3rd Qu.:5.037 3rd Qu.:0.5792426
## Max. :5.751 Max. :1.3542228
##
##
## $tables$NrCAM
## $tables$NrCAM$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1993
##
## x y
## Min. :2.447 Min. :0.0003116
## 1st Qu.:3.371 1st Qu.:0.0293994
## Median :4.295 Median :0.1835925
## Mean :4.295 Mean :0.2702652
## 3rd Qu.:5.219 3rd Qu.:0.5209232
## Max. :6.143 Max. :0.7108058
##
## $tables$NrCAM$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1695
##
## x y
## Min. :2.131 Min. :0.0001371
## 1st Qu.:3.228 1st Qu.:0.0241150
## Median :4.325 Median :0.0998974
## Mean :4.325 Mean :0.2276014
## 3rd Qu.:5.422 3rd Qu.:0.4393038
## Max. :6.520 Max. :0.6940378
##
##
## $tables$Osteopontin
## $tables$Osteopontin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1546
##
## x y
## Min. :3.647 Min. :0.000401
## 1st Qu.:4.427 1st Qu.:0.042471
## Median :5.208 Median :0.138436
## Mean :5.208 Mean :0.320090
## 3rd Qu.:5.988 3rd Qu.:0.668735
## Max. :6.768 Max. :0.924381
##
## $tables$Osteopontin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.104
##
## x y
## Min. :3.922 Min. :0.0002238
## 1st Qu.:4.597 1st Qu.:0.0540570
## Median :5.271 Median :0.2053005
## Mean :5.271 Mean :0.3702396
## 3rd Qu.:5.946 3rd Qu.:0.6556552
## Max. :6.620 Max. :1.1063334
##
##
## $tables$PAI_1
## $tables$PAI_1$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1684
##
## x y
## Min. :-1.3796 Min. :0.0003716
## 1st Qu.:-0.6169 1st Qu.:0.0434754
## Median : 0.1458 Median :0.2050729
## Mean : 0.1458 Mean :0.3274468
## 3rd Qu.: 0.9085 3rd Qu.:0.6656947
## Max. : 1.6713 Max. :0.7852475
##
## $tables$PAI_1$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1155
##
## x y
## Min. :-1.337358 Min. :0.0002027
## 1st Qu.:-0.665516 1st Qu.:0.0466148
## Median : 0.006326 Median :0.2555443
## Mean : 0.006326 Mean :0.3717397
## 3rd Qu.: 0.678169 3rd Qu.:0.6264542
## Max. : 1.350011 Max. :1.1007939
##
##
## $tables$PAPP_A
## $tables$PAPP_A$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.05245
##
## x y
## Min. :-3.293 Min. :0.001198
## 1st Qu.:-3.061 1st Qu.:0.114160
## Median :-2.828 Median :0.890903
## Mean :-2.828 Mean :1.073737
## 3rd Qu.:-2.596 3rd Qu.:2.084517
## Max. :-2.363 Max. :2.358653
##
## $tables$PAPP_A$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.04084
##
## x y
## Min. :-3.433 Min. :0.0005657
## 1st Qu.:-3.183 1st Qu.:0.0522086
## Median :-2.933 Median :0.7306600
## Mean :-2.933 Mean :0.9973974
## 3rd Qu.:-2.682 3rd Qu.:1.6228269
## Max. :-2.432 Max. :2.9265411
##
##
## $tables$PLGF
## $tables$PLGF$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1622
##
## x y
## Min. :1.998 Min. :0.0003797
## 1st Qu.:2.831 1st Qu.:0.0253253
## Median :3.665 Median :0.1726832
## Mean :3.665 Mean :0.2997800
## 3rd Qu.:4.498 3rd Qu.:0.5692687
## Max. :5.331 Max. :0.8946563
##
## $tables$PLGF$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1243
##
## x y
## Min. :2.571 Min. :0.0001858
## 1st Qu.:3.314 1st Qu.:0.0166248
## Median :4.057 Median :0.1949937
## Mean :4.057 Mean :0.3361260
## 3rd Qu.:4.800 3rd Qu.:0.6673277
## Max. :5.544 Max. :0.9770339
##
##
## $tables$PYY
## $tables$PYY$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.0982
##
## x y
## Min. :1.891 Min. :0.0006264
## 1st Qu.:2.455 1st Qu.:0.0587789
## Median :3.018 Median :0.1987673
## Mean :3.018 Mean :0.4433508
## 3rd Qu.:3.581 3rd Qu.:0.8178103
## Max. :4.145 Max. :1.3359805
##
## $tables$PYY$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08076
##
## x y
## Min. :2.060 Min. :0.0002882
## 1st Qu.:2.589 1st Qu.:0.0599845
## Median :3.117 Median :0.2062333
## Mean :3.117 Mean :0.4726133
## 3rd Qu.:3.646 3rd Qu.:0.9002350
## Max. :4.174 Max. :1.4121961
##
##
## $tables$Pancreatic_polypeptide
## $tables$Pancreatic_polypeptide$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2937
##
## x y
## Min. :-2.1541 Min. :0.000422
## 1st Qu.:-0.9124 1st Qu.:0.034616
## Median : 0.3293 Median :0.179068
## Mean : 0.3293 Mean :0.201128
## 3rd Qu.: 1.5710 3rd Qu.:0.321305
## Max. : 2.8126 Max. :0.521579
##
## $tables$Pancreatic_polypeptide$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.2106
##
## x y
## Min. :-2.7520 Min. :0.0001192
## 1st Qu.:-1.4939 1st Qu.:0.0178055
## Median :-0.2358 Median :0.1182623
## Mean :-0.2358 Mean :0.1985185
## 3rd Qu.: 1.0223 3rd Qu.:0.3752603
## Max. : 2.2803 Max. :0.5975853
##
##
## $tables$Prolactin
## $tables$Prolactin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.07877
##
## x y
## Min. :-0.7144 Min. :0.0007823
## 1st Qu.:-0.2476 1st Qu.:0.0770191
## Median : 0.2191 Median :0.1475766
## Mean : 0.2191 Mean :0.5350773
## 3rd Qu.: 0.6859 3rd Qu.:1.1812295
## Max. : 1.1526 Max. :1.5635536
##
## $tables$Prolactin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.09625
##
## x y
## Min. :-1.5981 Min. :0.000241
## 1st Qu.:-0.8781 1st Qu.:0.013587
## Median :-0.1580 Median :0.082170
## Mean :-0.1580 Mean :0.346865
## 3rd Qu.: 0.5620 3rd Qu.:0.594520
## Max. : 1.2820 Max. :1.351108
##
##
## $tables$Prostatic_Acid_Phosphatase
## $tables$Prostatic_Acid_Phosphatase$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.01973
##
## x y
## Min. :-1.831 Min. :0.003117
## 1st Qu.:-1.715 1st Qu.:0.250986
## Median :-1.598 Median :0.803856
## Mean :-1.598 Mean :2.141130
## 3rd Qu.:-1.481 3rd Qu.:3.681702
## Max. :-1.365 Max. :7.865372
##
## $tables$Prostatic_Acid_Phosphatase$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.01622
##
## x y
## Min. :-1.982 Min. :0.00143
## 1st Qu.:-1.862 1st Qu.:0.07955
## Median :-1.741 Median :0.74475
## Mean :-1.741 Mean :2.06975
## 3rd Qu.:-1.620 3rd Qu.:3.44902
## Max. :-1.500 Max. :8.16429
##
##
## $tables$Protein_S
## $tables$Protein_S$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.132
##
## x y
## Min. :-3.3850 Min. :0.0006421
## 1st Qu.:-2.7450 1st Qu.:0.0736992
## Median :-2.1050 Median :0.2183508
## Mean :-2.1050 Mean :0.3902128
## 3rd Qu.:-1.4650 3rd Qu.:0.7061450
## Max. :-0.8249 Max. :1.1818394
##
## $tables$Protein_S$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08967
##
## x y
## Min. :-3.607 Min. :0.0002593
## 1st Qu.:-2.954 1st Qu.:0.0470060
## Median :-2.300 Median :0.1650363
## Mean :-2.300 Mean :0.3821666
## 3rd Qu.:-1.647 3rd Qu.:0.6973574
## Max. :-0.993 Max. :1.3618423
##
##
## $tables$Pulmonary_and_Activation_Regulat
## $tables$Pulmonary_and_Activation_Regulat$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1511
##
## x y
## Min. :-2.75588 Min. :0.0004566
## 1st Qu.:-2.08129 1st Qu.:0.0629965
## Median :-1.40671 Median :0.2972115
## Mean :-1.40671 Mean :0.3702181
## 3rd Qu.:-0.73212 3rd Qu.:0.6554076
## Max. :-0.05753 Max. :0.8989888
##
## $tables$Pulmonary_and_Activation_Regulat$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1424
##
## x y
## Min. :-2.9405 Min. :0.0002549
## 1st Qu.:-2.1672 1st Qu.:0.0461147
## Median :-1.3939 Median :0.2342001
## Mean :-1.3939 Mean :0.3229625
## 3rd Qu.:-0.6206 3rd Qu.:0.5593760
## Max. : 0.1527 Max. :0.8766302
##
##
## $tables$RANTES
## $tables$RANTES$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1081
##
## x y
## Min. :-7.480 Min. :0.0005693
## 1st Qu.:-6.916 1st Qu.:0.0512402
## Median :-6.351 Median :0.2529665
## Mean :-6.351 Mean :0.4424479
## 3rd Qu.:-5.787 3rd Qu.:0.7600239
## Max. :-5.223 Max. :1.3911660
##
## $tables$RANTES$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1023
##
## x y
## Min. :-7.529 Min. :0.0002668
## 1st Qu.:-6.964 1st Qu.:0.0545166
## Median :-6.398 Median :0.2597945
## Mean :-6.398 Mean :0.4413177
## 3rd Qu.:-5.832 3rd Qu.:0.8946439
## Max. :-5.266 Max. :1.0897737
##
##
## $tables$Resistin
## $tables$Resistin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 2.203
##
## x y
## Min. :-41.576 Min. :2.786e-05
## 1st Qu.:-30.090 1st Qu.:2.151e-03
## Median :-18.603 Median :1.135e-02
## Mean :-18.603 Mean :2.174e-02
## 3rd Qu.: -7.116 3rd Qu.:4.037e-02
## Max. : 4.370 Max. :7.088e-02
##
## $tables$Resistin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 1.737
##
## x y
## Min. :-37.350 Min. :1.397e-05
## 1st Qu.:-27.539 1st Qu.:2.853e-03
## Median :-17.728 Median :1.742e-02
## Mean :-17.728 Mean :2.546e-02
## 3rd Qu.: -7.917 3rd Qu.:4.772e-02
## Max. : 1.895 Max. :6.884e-02
##
##
## $tables$S100b
## $tables$S100b$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1279
##
## x y
## Min. :0.1209 Min. :0.0004918
## 1st Qu.:0.6790 1st Qu.:0.0631910
## Median :1.2371 Median :0.3861436
## Mean :1.2371 Mean :0.4474792
## 3rd Qu.:1.7952 3rd Qu.:0.8303564
## Max. :2.3533 Max. :1.0540312
##
## $tables$S100b$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1065
##
## x y
## Min. :-0.1322 Min. :0.0002179
## 1st Qu.: 0.5739 1st Qu.:0.0168011
## Median : 1.2800 Median :0.1520255
## Mean : 1.2800 Mean :0.3537068
## 3rd Qu.: 1.9861 3rd Qu.:0.7299820
## Max. : 2.6922 Max. :1.0695961
##
##
## $tables$SGOT
## $tables$SGOT$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1086
##
## x y
## Min. :-1.2934 Min. :0.0006252
## 1st Qu.:-0.8045 1st Qu.:0.0859353
## Median :-0.3156 Median :0.3799067
## Mean :-0.3156 Mean :0.5108086
## 3rd Qu.: 0.1734 3rd Qu.:0.8986570
## Max. : 0.6623 Max. :1.4000863
##
## $tables$SGOT$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1027
##
## x y
## Min. :-1.6551 Min. :0.0002268
## 1st Qu.:-0.9788 1st Qu.:0.0479454
## Median :-0.3026 Median :0.1820441
## Mean :-0.3026 Mean :0.3693057
## 3rd Qu.: 0.3737 3rd Qu.:0.6595432
## Max. : 1.0500 Max. :1.1205080
##
##
## $tables$SHBG
## $tables$SHBG$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.193
##
## x y
## Min. :-4.3088 Min. :0.0003207
## 1st Qu.:-3.3640 1st Qu.:0.0397909
## Median :-2.4192 Median :0.1812374
## Mean :-2.4192 Mean :0.2643364
## 3rd Qu.:-1.4744 3rd Qu.:0.4392962
## Max. :-0.5296 Max. :0.7662495
##
## $tables$SHBG$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1561
##
## x y
## Min. :-4.6033 Min. :0.000192
## 1st Qu.:-3.6203 1st Qu.:0.035880
## Median :-2.6373 Median :0.173500
## Mean :-2.6373 Mean :0.254064
## 3rd Qu.:-1.6543 3rd Qu.:0.413084
## Max. :-0.6713 Max. :0.750277
##
##
## $tables$SOD
## $tables$SOD$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.136
##
## x y
## Min. :3.923 Min. :0.0004526
## 1st Qu.:4.623 1st Qu.:0.0414213
## Median :5.324 Median :0.1455296
## Mean :5.324 Mean :0.3564745
## 3rd Qu.:6.025 3rd Qu.:0.6707309
## Max. :6.725 Max. :1.2210852
##
## $tables$SOD$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1159
##
## x y
## Min. :3.970 Min. :0.0002493
## 1st Qu.:4.643 1st Qu.:0.0622886
## Median :5.317 Median :0.2167046
## Mean :5.317 Mean :0.3706429
## 3rd Qu.:5.991 3rd Qu.:0.6627376
## Max. :6.665 Max. :1.0821738
##
##
## $tables$Serum_Amyloid_P
## $tables$Serum_Amyloid_P$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2052
##
## x y
## Min. :-7.954 Min. :0.0003267
## 1st Qu.:-7.013 1st Qu.:0.0449062
## Median :-6.071 Median :0.2264938
## Mean :-6.071 Mean :0.2652633
## 3rd Qu.:-5.130 3rd Qu.:0.4609918
## Max. :-4.188 Max. :0.6740704
##
## $tables$Serum_Amyloid_P$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1606
##
## x y
## Min. :-7.987 Min. :0.0001543
## 1st Qu.:-7.032 1st Qu.:0.0439265
## Median :-6.076 Median :0.1504869
## Mean :-6.076 Mean :0.2613082
## 3rd Qu.:-5.120 3rd Qu.:0.4683075
## Max. :-4.164 Max. :0.7420157
##
##
## $tables$Sortilin
## $tables$Sortilin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.3258
##
## x y
## Min. :1.364 Min. :0.0001889
## 1st Qu.:2.824 1st Qu.:0.0197280
## Median :4.283 Median :0.1412408
## Mean :4.283 Mean :0.1710930
## 3rd Qu.:5.743 3rd Qu.:0.3173361
## Max. :7.203 Max. :0.4405171
##
## $tables$Sortilin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.2559
##
## x y
## Min. :0.886 Min. :0.0000906
## 1st Qu.:2.413 1st Qu.:0.0185690
## Median :3.940 Median :0.1084897
## Mean :3.940 Mean :0.1635817
## 3rd Qu.:5.466 3rd Qu.:0.2846376
## Max. :6.993 Max. :0.4849456
##
##
## $tables$Stem_Cell_Factor
## $tables$Stem_Cell_Factor$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1255
##
## x y
## Min. :2.021 Min. :0.0004942
## 1st Qu.:2.598 1st Qu.:0.0566899
## Median :3.175 Median :0.3032416
## Mean :3.175 Mean :0.4331087
## 3rd Qu.:3.751 3rd Qu.:0.8939820
## Max. :4.328 Max. :1.1322102
##
## $tables$Stem_Cell_Factor$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1128
##
## x y
## Min. :1.913 Min. :0.0002063
## 1st Qu.:2.588 1st Qu.:0.0370097
## Median :3.264 Median :0.2179984
## Mean :3.264 Mean :0.3696697
## 3rd Qu.:3.940 3rd Qu.:0.7351579
## Max. :4.615 Max. :1.0136717
##
##
## $tables$TGF_alpha
## $tables$TGF_alpha$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.4318
##
## x y
## Min. : 5.819 Min. :0.0001796
## 1st Qu.: 7.893 1st Qu.:0.0241862
## Median : 9.968 Median :0.0676654
## Mean : 9.968 Mean :0.1203657
## 3rd Qu.:12.043 3rd Qu.:0.2279201
## Max. :14.118 Max. :0.3197847
##
## $tables$TGF_alpha$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.4265
##
## x y
## Min. : 5.563 Min. :5.482e-05
## 1st Qu.: 7.949 1st Qu.:9.882e-03
## Median :10.335 Median :7.688e-02
## Mean :10.335 Mean :1.047e-01
## 3rd Qu.:12.721 3rd Qu.:1.933e-01
## Max. :15.107 Max. :3.051e-01
##
##
## $tables$TIMP_1
## $tables$TIMP_1$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.7631
##
## x y
## Min. : 6.665 Min. :8.067e-05
## 1st Qu.:10.291 1st Qu.:5.943e-03
## Median :13.918 Median :4.034e-02
## Mean :13.918 Mean :6.887e-02
## 3rd Qu.:17.544 3rd Qu.:1.317e-01
## Max. :21.170 Max. :1.903e-01
##
## $tables$TIMP_1$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.5226
##
## x y
## Min. : 0.1739 Min. :0.0000000
## 1st Qu.: 4.9973 1st Qu.:0.0002643
## Median : 9.8207 Median :0.0067064
## Mean : 9.8207 Mean :0.0517794
## 3rd Qu.:14.6441 3rd Qu.:0.0813404
## Max. :19.4675 Max. :0.2372709
##
##
## $tables$TNF_RII
## $tables$TNF_RII$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1287
##
## x y
## Min. :-1.7724 Min. :0.0004771
## 1st Qu.:-1.1153 1st Qu.:0.0399400
## Median :-0.4581 Median :0.1770322
## Mean :-0.4581 Mean :0.3800557
## 3rd Qu.: 0.1990 3rd Qu.:0.7852383
## Max. : 0.8561 Max. :1.0887398
##
## $tables$TNF_RII$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.09976
##
## x y
## Min. :-1.96002 Min. :0.0002318
## 1st Qu.:-1.31108 1st Qu.:0.0217256
## Median :-0.66213 Median :0.1370032
## Mean :-0.66213 Mean :0.3848579
## 3rd Qu.:-0.01318 3rd Qu.:0.7887963
## Max. : 0.63577 Max. :1.1798407
##
##
## $tables$TRAIL_R3
## $tables$TRAIL_R3$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.08757
##
## x y
## Min. :-1.1644 Min. :0.0007029
## 1st Qu.:-0.7402 1st Qu.:0.0607016
## Median :-0.3161 Median :0.3646131
## Mean :-0.3161 Mean :0.5888709
## 3rd Qu.: 0.1080 3rd Qu.:1.1517577
## Max. : 0.5321 Max. :1.6025082
##
## $tables$TRAIL_R3$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.071
##
## x y
## Min. :-1.42370 Min. :0.0003268
## 1st Qu.:-0.96810 1st Qu.:0.0299330
## Median :-0.51251 Median :0.2244368
## Mean :-0.51251 Mean :0.5481931
## 3rd Qu.:-0.05692 3rd Qu.:1.1332933
## Max. : 0.39867 Max. :1.6608826
##
##
## $tables$TTR_prealbumin
## $tables$TTR_prealbumin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.03354
##
## x y
## Min. :2.464 Min. :0.000521
## 1st Qu.:2.706 1st Qu.:0.064652
## Median :2.949 Median :0.514918
## Mean :2.949 Mean :1.031472
## 3rd Qu.:3.191 3rd Qu.:1.629581
## Max. :3.433 Max. :3.468656
##
## $tables$TTR_prealbumin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.04025
##
## x y
## Min. :2.364 Min. :0.0005777
## 1st Qu.:2.618 1st Qu.:0.0875722
## Median :2.872 Median :0.6405262
## Mean :2.872 Mean :0.9845682
## 3rd Qu.:3.125 3rd Qu.:1.7027102
## Max. :3.379 Max. :2.9232996
##
##
## $tables$Tamm_Horsfall_Protein_THP
## $tables$Tamm_Horsfall_Protein_THP$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.01
##
## x y
## Min. :-3.236 Min. : 0.006154
## 1st Qu.:-3.179 1st Qu.: 0.388728
## Median :-3.123 Median : 2.717438
## Mean :-3.123 Mean : 4.447218
## 3rd Qu.:-3.067 3rd Qu.: 7.914170
## Max. :-3.011 Max. :13.417891
##
## $tables$Tamm_Horsfall_Protein_THP$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.01004
##
## x y
## Min. :-3.236 Min. : 0.002311
## 1st Qu.:-3.168 1st Qu.: 0.202415
## Median :-3.100 Median : 1.442769
## Mean :-3.100 Mean : 3.682889
## 3rd Qu.:-3.032 3rd Qu.: 6.965682
## Max. :-2.964 Max. :12.309838
##
##
## $tables$Thrombomodulin
## $tables$Thrombomodulin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.08265
##
## x y
## Min. :-2.2233 Min. :0.0007845
## 1st Qu.:-1.8097 1st Qu.:0.0790310
## Median :-1.3960 Median :0.2426811
## Mean :-1.3960 Mean :0.6037388
## 3rd Qu.:-0.9823 3rd Qu.:1.2856953
## Max. :-0.5687 Max. :1.8626843
##
## $tables$Thrombomodulin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.07006
##
## x y
## Min. :-2.2478 Min. :0.0005805
## 1st Qu.:-1.8819 1st Qu.:0.1115174
## Median :-1.5160 Median :0.5892656
## Mean :-1.5160 Mean :0.6825325
## 3rd Qu.:-1.1501 3rd Qu.:1.1908053
## Max. :-0.7842 Max. :1.7654128
##
##
## $tables$Thrombopoietin
## $tables$Thrombopoietin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.06773
##
## x y
## Min. :-1.74275 Min. :0.0009095
## 1st Qu.:-1.32325 1st Qu.:0.0452164
## Median :-0.90376 Median :0.3490210
## Mean :-0.90376 Mean :0.5953374
## 3rd Qu.:-0.48426 3rd Qu.:0.9919720
## Max. :-0.06476 Max. :2.1041470
##
## $tables$Thrombopoietin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.07041
##
## x y
## Min. :-1.5184 Min. :0.0003296
## 1st Qu.:-1.0616 1st Qu.:0.0349606
## Median :-0.6048 Median :0.3482742
## Mean :-0.6048 Mean :0.5467200
## 3rd Qu.:-0.1480 3rd Qu.:0.9245078
## Max. : 0.3089 Max. :1.7662263
##
##
## $tables$Thymus_Expressed_Chemokine_TECK
## $tables$Thymus_Expressed_Chemokine_TECK$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2555
##
## x y
## Min. :1.170 Min. :0.0002411
## 1st Qu.:2.625 1st Qu.:0.0178690
## Median :4.081 Median :0.0793885
## Mean :4.081 Mean :0.1715881
## 3rd Qu.:5.536 3rd Qu.:0.3113422
## Max. :6.992 Max. :0.5602818
##
## $tables$Thymus_Expressed_Chemokine_TECK$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1889
##
## x y
## Min. :0.9418 Min. :0.0001244
## 1st Qu.:2.4043 1st Qu.:0.0171162
## Median :3.8669 Median :0.0723034
## Mean :3.8669 Mean :0.1707644
## 3rd Qu.:5.3294 3rd Qu.:0.2915050
## Max. :6.7920 Max. :0.5972220
##
##
## $tables$Thyroid_Stimulating_Hormone
## $tables$Thyroid_Stimulating_Hormone$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.3209
##
## x y
## Min. :-7.152 Min. :0.000193
## 1st Qu.:-5.704 1st Qu.:0.026237
## Median :-4.256 Median :0.112350
## Mean :-4.256 Mean :0.172455
## 3rd Qu.:-2.808 3rd Qu.:0.326685
## Max. :-1.360 Max. :0.463520
##
## $tables$Thyroid_Stimulating_Hormone$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1916
##
## x y
## Min. :-6.765 Min. :0.0001212
## 1st Qu.:-5.359 1st Qu.:0.0106355
## Median :-3.952 Median :0.0640522
## Mean :-3.952 Mean :0.1775933
## 3rd Qu.:-2.546 3rd Qu.:0.3428797
## Max. :-1.140 Max. :0.6111553
##
##
## $tables$Thyroxine_Binding_Globulin
## $tables$Thyroxine_Binding_Globulin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1455
##
## x y
## Min. :-2.5566 Min. :0.0009009
## 1st Qu.:-1.9624 1st Qu.:0.0891957
## Median :-1.3682 Median :0.4052720
## Mean :-1.3682 Mean :0.4202828
## 3rd Qu.:-0.7740 3rd Qu.:0.7468665
## Max. :-0.1798 Max. :0.8914414
##
## $tables$Thyroxine_Binding_Globulin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1246
##
## x y
## Min. :-2.8507 Min. :0.0001861
## 1st Qu.:-2.0973 1st Qu.:0.0276999
## Median :-1.3438 Median :0.1559497
## Mean :-1.3438 Mean :0.3314880
## 3rd Qu.:-0.5904 3rd Qu.:0.6693230
## Max. : 0.1630 Max. :0.9621225
##
##
## $tables$Tissue_Factor
## $tables$Tissue_Factor$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1853
##
## x y
## Min. :-0.5658 Min. :0.0003866
## 1st Qu.: 0.2583 1st Qu.:0.0448630
## Median : 1.0824 Median :0.2176691
## Mean : 1.0824 Mean :0.3030555
## 3rd Qu.: 1.9064 3rd Qu.:0.5405102
## Max. : 2.7305 Max. :0.8277802
##
## $tables$Tissue_Factor$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1519
##
## x y
## Min. :-0.6665 Min. :0.0001846
## 1st Qu.: 0.2353 1st Qu.:0.0412224
## Median : 1.1371 Median :0.1699385
## Mean : 1.1371 Mean :0.2769517
## 3rd Qu.: 2.0389 3rd Qu.:0.4979988
## Max. : 2.9407 Max. :0.8128868
##
##
## $tables$Transferrin
## $tables$Transferrin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.09068
##
## x y
## Min. :1.659 Min. :0.0006793
## 1st Qu.:2.194 1st Qu.:0.0261394
## Median :2.729 Median :0.1797464
## Mean :2.729 Mean :0.4670484
## 3rd Qu.:3.264 3rd Qu.:0.8490787
## Max. :3.798 Max. :1.6587180
##
## $tables$Transferrin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08756
##
## x y
## Min. :1.683 Min. :0.0002639
## 1st Qu.:2.268 1st Qu.:0.0213932
## Median :2.854 Median :0.1753310
## Mean :2.854 Mean :0.4268084
## 3rd Qu.:3.439 3rd Qu.:0.7879761
## Max. :4.024 Max. :1.4700049
##
##
## $tables$Trefoil_Factor_3_TFF3
## $tables$Trefoil_Factor_3_TFF3$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1387
##
## x y
## Min. :-5.160 Min. :0.0004691
## 1st Qu.:-4.505 1st Qu.:0.0602439
## Median :-3.850 Median :0.2162550
## Mean :-3.850 Mean :0.3813086
## 3rd Qu.:-3.196 3rd Qu.:0.7332299
## Max. :-2.541 Max. :1.0672378
##
## $tables$Trefoil_Factor_3_TFF3$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1031
##
## x y
## Min. :-4.987 Min. :0.0002692
## 1st Qu.:-4.422 1st Qu.:0.0576603
## Median :-3.857 Median :0.3071860
## Mean :-3.857 Mean :0.4420550
## 3rd Qu.:-3.292 3rd Qu.:0.8687531
## Max. :-2.727 Max. :1.1003838
##
##
## $tables$VCAM_1
## $tables$VCAM_1$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1398
##
## x y
## Min. :1.304 Min. :0.0004403
## 1st Qu.:2.005 1st Qu.:0.0358126
## Median :2.706 Median :0.1836501
## Mean :2.706 Mean :0.3561936
## 3rd Qu.:3.407 3rd Qu.:0.5714179
## Max. :4.108 Max. :1.1471843
##
## $tables$VCAM_1$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.09411
##
## x y
## Min. :1.649 Min. :0.0003481
## 1st Qu.:2.217 1st Qu.:0.0295382
## Median :2.785 Median :0.2424928
## Mean :2.785 Mean :0.4399457
## 3rd Qu.:3.352 3rd Qu.:0.8122340
## Max. :3.920 Max. :1.3063664
##
##
## $tables$VEGF
## $tables$VEGF$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.5539
##
## x y
## Min. :11.04 Min. :0.0001241
## 1st Qu.:13.97 1st Qu.:0.0183353
## Median :16.90 Median :0.0447208
## Mean :16.90 Mean :0.0853111
## 3rd Qu.:19.82 3rd Qu.:0.1614892
## Max. :22.75 Max. :0.2623878
##
## $tables$VEGF$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.523
##
## x y
## Min. :10.26 Min. :4.443e-05
## 1st Qu.:13.68 1st Qu.:8.392e-03
## Median :17.11 Median :3.548e-02
## Mean :17.11 Mean :7.299e-02
## 3rd Qu.:20.53 3rd Qu.:1.281e-01
## Max. :23.95 Max. :2.298e-01
##
##
## $tables$Vitronectin
## $tables$Vitronectin$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.116
##
## x y
## Min. :-1.2896 Min. :0.0006976
## 1st Qu.:-0.7961 1st Qu.:0.0864891
## Median :-0.3026 Median :0.4314761
## Mean :-0.3026 Mean :0.5060632
## 3rd Qu.: 0.1909 3rd Qu.:0.9488585
## Max. : 0.6844 Max. :1.1560427
##
## $tables$Vitronectin$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1072
##
## x y
## Min. :-1.7486 Min. :0.0002163
## 1st Qu.:-1.0984 1st Qu.:0.0194681
## Median :-0.4482 Median :0.1803515
## Mean :-0.4482 Mean :0.3841159
## 3rd Qu.: 0.2020 3rd Qu.:0.8247959
## Max. : 0.8522 Max. :1.0697444
##
##
## $tables$von_Willebrand_Factor
## $tables$von_Willebrand_Factor$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1421
##
## x y
## Min. :-5.182 Min. :0.0004347
## 1st Qu.:-4.519 1st Qu.:0.0444301
## Median :-3.856 Median :0.3047486
## Mean :-3.856 Mean :0.3766881
## 3rd Qu.:-3.193 3rd Qu.:0.6974241
## Max. :-2.530 Max. :0.9165692
##
## $tables$von_Willebrand_Factor$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1166
##
## x y
## Min. :-5.341 Min. :0.0002463
## 1st Qu.:-4.688 1st Qu.:0.0402466
## Median :-4.035 Median :0.2615668
## Mean :-4.035 Mean :0.3825643
## 3rd Qu.:-3.382 3rd Qu.:0.7507236
## Max. :-2.729 Max. :0.9994338
##
##
## $tables$E4
## $tables$E4$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.189
##
## x y
## Min. :0.4329 Min. :0.009735
## 1st Qu.:0.9664 1st Qu.:0.114497
## Median :1.5000 Median :0.380075
## Mean :1.5000 Mean :0.467481
## 3rd Qu.:2.0336 3rd Qu.:0.781582
## Max. :2.5671 Max. :1.242688
##
## $tables$E4$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1479
##
## x y
## Min. :0.5562 Min. :0.008458
## 1st Qu.:1.0281 1st Qu.:0.071644
## Median :1.5000 Median :0.346103
## Mean :1.5000 Mean :0.528563
## 3rd Qu.:1.9719 3rd Qu.:0.834386
## Max. :2.4438 Max. :1.806543
##
##
## $tables$E3
## $tables$E3$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.12
##
## x y
## Min. :0.6399 Min. :0.00035
## 1st Qu.:1.0700 1st Qu.:0.02443
## Median :1.5000 Median :0.17519
## Mean :1.5000 Mean :0.58003
## 3rd Qu.:1.9300 3rd Qu.:0.59273
## Max. :2.3601 Max. :2.95885
##
## $tables$E3$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08141
##
## x y
## Min. :0.7558 Min. :0.000000
## 1st Qu.:1.1279 1st Qu.:0.000745
## Median :1.5000 Median :0.079069
## Mean :1.5000 Mean :0.670302
## 3rd Qu.:1.8721 3rd Qu.:0.353347
## Max. :2.2442 Max. :4.544953
##
##
## $tables$E2
## $tables$E2$Impaired
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1055
##
## x y
## Min. :0.6834 Min. :0.000028
## 1st Qu.:1.0917 1st Qu.:0.010565
## Median :1.5000 Median :0.130726
## Mean :1.5000 Mean :0.610915
## 3rd Qu.:1.9083 3rd Qu.:0.531913
## Max. :2.3166 Max. :3.468093
##
## $tables$E2$Control
##
## Call:
## density.default(x = xx, adjust = ..1)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1236
##
## x y
## Min. :0.6292 Min. :0.0007128
## 1st Qu.:1.0646 1st Qu.:0.0372768
## Median :1.5000 Median :0.2468160
## Mean :1.5000 Mean :0.5728558
## 3rd Qu.:1.9354 3rd Qu.:0.6137651
## Max. :2.3708 Max. :2.6115601
##
##
##
## $levels
## [1] "Impaired" "Control"
##
## $call
## NaiveBayes.default(x = x, grouping = y, usekernel = TRUE, fL = param$fL,
## adjust = param$adjust)
##
## $x
## ACE_CD143_Angiotensin_Converti ACTH_Adrenocorticotropic_Hormon AXL
## X1 2.00310035 -1.3862944 1.09838668
## X2 1.56185602 -1.3862944 0.68328157
## X3 1.52065981 -1.7147984 -0.14527630
## X5 2.40093083 -0.9675840 0.19089023
## X6 0.43115645 -1.2729657 -0.22236112
## X7 0.94620673 -1.8971200 0.52982213
## X8 0.70781531 -1.8325815 -0.32667995
## X9 1.10654173 -1.9661129 0.19089023
## X11 1.82987064 -0.9942523 0.36643191
## X12 1.00119771 -1.7147984 0.36643191
## X14 1.52065981 -1.7147984 0.19089023
## X16 1.86560359 -1.6607312 0.60768096
## X17 0.88951564 -1.6094379 0.19089023
## X18 0.50475301 -1.8971200 -0.16696972
## X19 0.43115645 -1.7719568 -0.60000000
## X20 0.88951564 -1.7719568 -0.64353400
## X21 0.94620673 -1.1086626 -0.15609111
## X22 2.03621269 -1.6094379 0.44948974
## X23 1.20635622 -1.5141277 0.09761770
## X24 1.79355115 -1.6094379 1.22490310
## X25 1.05460735 -1.5141277 -0.07126985
## X26 1.79355115 -1.3470736 0.60768096
## X28 1.43572821 -1.7147984 0.89827535
## X29 0.43115645 -1.6094379 -0.27953495
## X30 1.30129723 -1.8971200 -0.09212160
## X31 1.25439977 -1.4696760 0.19089023
## X34 0.57519641 -2.2072749 -0.12383370
## X35 0.70781531 -1.8971200 -0.07126985
## X36 1.15709609 -1.7147984 0.28035085
## X37 1.82987064 -1.4696760 0.60768096
## X38 1.25439977 -1.6094379 0.09761770
## X39 2.28543598 -1.1711830 0.68328157
## X40 1.56185602 -1.2729657 0.09761770
## X41 0.64279595 -1.8971200 0.28035085
## X42 1.30129723 -1.2378744 0.60768096
## X43 0.77048138 -1.7719568 -0.08166739
## X44 1.39190522 -1.1711830 0.44948974
## X45 1.20635622 -1.5141277 0.52982213
## X46 1.90077250 -1.5141277 0.75680975
## X47 0.57519641 -1.8971200 -0.30294373
## X48 1.79355115 -1.2378744 1.09838668
## X50 1.90077250 -0.8675006 0.28035085
## X51 1.25439977 -1.7719568 0.60768096
## X53 1.20635622 -1.3470736 -0.02010101
## X55 1.47863123 -1.4271164 0.52982213
## X56 1.05460735 -2.1202635 -0.04040821
## X57 0.64279595 -1.0498221 0.68328157
## X59 1.05460735 -2.2072749 -0.12383370
## X60 0.50475301 -2.1202635 -0.46377085
## X61 0.43115645 -1.2729657 -0.31477005
## X62 0.77048138 -1.8325815 -0.04040821
## X63 1.10654173 -1.7147984 0.19089023
## X64 0.94620673 -1.8325815 0.00000000
## X65 1.82987064 -1.4696760 0.44948974
## X67 1.71905522 -1.8971200 0.60768096
## X68 1.43572821 -1.4271164 0.60768096
## X69 0.64279595 -1.7147984 -0.33867523
## X70 1.25439977 -1.5141277 0.60768096
## X71 1.64190425 -1.5141277 0.09761770
## X72 2.31482865 -1.3862944 0.52982213
## X73 1.90077250 -1.3470736 0.82842712
## X74 0.09668586 -1.6607312 -0.68851230
## X75 0.70781531 -1.7719568 -0.21114562
## X76 2.03621269 -1.9661129 1.03315018
## X77 1.71905522 -1.4271164 0.19089023
## X78 1.68082597 -1.2729657 1.22490310
## X80 1.43572821 -1.4696760 0.09761770
## X81 1.64190425 -1.6094379 0.19089023
## X82 0.43115645 -1.5141277 0.52982213
## X83 1.96950153 -1.3470736 0.52982213
## X84 0.64279595 -1.8325815 -0.02010101
## X85 1.71905522 -1.7147984 1.22490310
## X86 1.30129723 -1.2378744 0.89827535
## X88 2.03621269 -1.2729657 0.19089023
## X90 -0.67562020 -1.9661129 -0.64353400
## X93 1.00119771 -1.6607312 0.28035085
## X94 1.64190425 -1.4696760 1.22490310
## X95 1.15709609 -1.2729657 -0.15609111
## X96 1.71905522 -0.9416085 0.09761770
## X97 2.03621269 -1.3093333 0.36643191
## X98 0.77048138 -1.6607312 -0.65835921
## X99 1.79355115 -1.7147984 0.09761770
## X100 1.75662119 -1.2729657 0.89827535
## X103 2.40093083 -1.7147984 0.75680975
## X104 1.10654173 -1.6094379 0.44948974
## X105 1.30129723 -1.3862944 0.89827535
## X107 1.64190425 -1.8325815 0.60768096
## X108 1.34711282 -1.8971200 0.60768096
## X109 0.94620673 -1.6094379 -0.16696972
## X110 1.00119771 -1.8325815 -0.22236112
## X111 1.96950153 -1.3470736 0.60768096
## X112 1.64190425 -1.7147984 0.82842712
## X113 1.43572821 -1.5606477 0.28035085
## X114 0.27296583 -1.5606477 -0.09212160
## X115 0.35404039 -2.1202635 0.19089023
## X117 1.93539850 -1.5141277 1.03315018
## X118 1.96950153 -1.1086626 0.68328157
## X121 1.71905522 -1.8971200 0.36643191
## X123 0.43115645 -1.3093333 -0.61435935
## X124 1.25439977 -1.2729657 -0.04040821
## X126 0.57519641 -1.6094379 0.00000000
## X128 1.39190522 -1.4271164 0.96647939
## X129 0.43115645 -1.7719568 -0.37519232
## X130 1.75662119 -1.2378744 0.52982213
## X131 1.15709609 -1.5141277 0.19089023
## X132 0.94620673 -1.7147984 0.36643191
## X133 1.25439977 -1.5606477 0.36643191
## X134 0.88951564 -1.2378744 0.68328157
## X135 1.60225879 -1.8971200 0.52982213
## X136 1.25439977 -1.5606477 0.00000000
## X137 1.05460735 -1.3093333 -0.15609111
## X139 1.20635622 -1.0216512 -0.18892297
## X140 1.86560359 -1.3862944 0.60768096
## X141 2.03621269 -1.5606477 0.75680975
## X143 0.50475301 -1.8325815 -0.11320377
## X144 0.83099088 -1.2729657 0.28035085
## X145 1.43572821 -1.8325815 0.36643191
## X146 1.34711282 -1.9661129 0.75680975
## X147 1.79355115 -1.5606477 0.60768096
## X148 1.39190522 -1.3862944 0.75680975
## X149 1.10654173 -1.3862944 0.19089023
## X152 2.16411943 -1.4696760 1.52136337
## X153 1.71905522 -1.3470736 0.19089023
## X154 1.10654173 -1.3862944 0.09761770
## X155 1.34711282 -1.4271164 0.00000000
## X156 2.06885532 -1.4696760 0.36643191
## X157 1.47863123 -1.8971200 0.09761770
## X158 1.43572821 -1.3862944 0.36643191
## X159 1.43572821 -1.6094379 0.19089023
## X160 2.83982088 -1.2729657 0.89827535
## X161 1.15709609 -1.6094379 0.68328157
## X162 1.71905522 -0.8675006 0.52982213
## X163 1.34711282 -1.0788097 -0.01002513
## X165 1.79355115 -1.5606477 0.09761770
## X166 1.64190425 -1.6607312 0.44948974
## X167 2.25568010 -1.5141277 1.28633535
## X168 1.39190522 -1.6094379 0.52982213
## X169 0.88951564 -1.3093333 0.19089023
## X170 0.57519641 -1.5606477 -0.09212160
## X171 1.86560359 -1.2039728 0.89827535
## X172 1.43572821 -1.5606477 0.36643191
## X174 1.96950153 -1.9661129 0.00000000
## X175 1.47863123 -1.2378744 0.52982213
## X176 1.43572821 -1.7147984 -0.11320377
## X177 1.52065981 -1.6607312 0.44948974
## X178 1.10654173 -1.8971200 -0.11320377
## X179 1.52065981 -1.4696760 0.44948974
## X180 1.96950153 -1.4271164 0.82842712
## X181 0.57519641 -1.5606477 -0.55777949
## X182 1.15709609 -1.8325815 0.82842712
## X183 0.64279595 -2.1202635 -0.38754845
## X184 1.68082597 -1.5141277 0.68328157
## X185 1.34711282 -1.7147984 -0.03022844
## X186 1.68082597 -1.6094379 0.19089023
## X189 0.43115645 -1.5141277 0.36643191
## X190 1.79355115 -1.6607312 0.75680975
## X191 1.30129723 -1.6607312 0.52982213
## X192 2.25568010 -1.8971200 1.16227766
## X193 1.39190522 -2.0402208 0.36643191
## X194 1.15709609 -1.3470736 0.19089023
## X195 2.51123919 -1.3862944 1.22490310
## X197 1.96950153 -1.2039728 0.52982213
## X198 1.30129723 -1.3470736 0.00000000
## X200 1.39190522 -1.0216512 0.68328157
## X201 0.70781531 -1.2378744 -0.02010101
## X202 1.15709609 -1.2378744 0.09761770
## X205 1.86560359 -1.2729657 0.52982213
## X208 1.15709609 -1.7719568 0.52982213
## X210 1.90077250 -1.1711830 0.52982213
## X212 0.94620673 -1.7719568 0.09761770
## X213 1.20635622 -1.3470736 0.52982213
## X214 1.15709609 -1.5141277 0.09761770
## X215 1.05460735 -1.5141277 0.09761770
## X216 1.10654173 -1.5606477 0.09761770
## X218 0.35404039 -1.4696760 0.28035085
## X219 0.83099088 -1.3093333 0.19089023
## X220 1.39190522 -1.7719568 0.68328157
## X223 1.82987064 -1.6094379 0.44948974
## X224 1.34711282 -2.1202635 0.09761770
## X225 1.82987064 -1.3862944 1.09838668
## X226 2.03621269 -1.8325815 1.22490310
## X227 1.64190425 -1.6094379 0.52982213
## X228 1.10654173 -1.7719568 -0.22236112
## X229 0.88951564 -1.3470736 0.00000000
## X230 1.39190522 -1.8325815 0.19089023
## X231 1.10654173 -1.2039728 0.36643191
## X232 1.30129723 -1.5606477 -0.09212160
## X233 0.70781531 -1.7719568 -0.16696972
## X234 1.30129723 -1.5606477 0.19089023
## X236 1.25439977 -1.1086626 0.19089023
## X237 1.15709609 -1.4271164 0.36643191
## X239 0.50475301 -1.8971200 -0.67335008
## X240 1.15709609 -1.8325815 -0.11320377
## X241 1.68082597 -1.7147984 0.82842712
## X242 1.56185602 -1.5606477 -0.45080666
## X243 0.64279595 -2.1202635 0.00000000
## X244 1.56185602 -1.6607312 0.28035085
## X245 0.64279595 -1.3862944 -0.24500712
## X246 1.05460735 -1.3862944 -0.25644042
## X247 1.43572821 -1.3470736 -0.05064113
## X249 2.13279405 -1.5606477 1.28633535
## X250 0.35404039 -1.4271164 0.19089023
## X251 0.77048138 -1.9661129 0.19089023
## X253 0.94620673 -1.4696760 0.28035085
## X254 2.64263321 -1.4696760 1.09838668
## X255 1.90077250 -1.1086626 0.96647939
## X256 1.15709609 -1.5141277 0.36643191
## X257 1.05460735 -0.9675840 -0.14527630
## X258 0.43115645 -1.6094379 0.09761770
## X260 1.47863123 -1.6094379 0.52982213
## X261 1.15709609 -1.7147984 -0.13452419
## X262 1.15709609 -1.5141277 0.00000000
## X263 2.13279405 -1.3862944 1.16227766
## X264 0.77048138 -1.9661129 0.19089023
## X265 1.25439977 -1.6607312 0.68328157
## X267 1.47863123 -1.5606477 0.89827535
## X268 1.71905522 -1.3470736 0.44948974
## X269 1.00119771 -1.7719568 0.09761770
## X270 2.03621269 -1.8325815 1.09838668
## X271 0.70781531 -1.0788097 0.19089023
## X272 1.86560359 -1.5141277 0.60768096
## X273 1.68082597 -1.1711830 0.52982213
## X274 1.34711282 -1.5606477 0.60768096
## X275 1.93539850 -1.0788097 0.60768096
## X277 1.10654173 -1.7147984 0.19089023
## X278 0.70781531 -1.8971200 0.52982213
## X279 1.25439977 -1.4271164 0.68328157
## X281 1.60225879 -1.3862944 0.52982213
## X282 0.77048138 -1.3862944 0.19089023
## X283 2.25568010 -1.4271164 0.82842712
## X287 1.79355115 -1.5606477 0.44948974
## X289 1.10654173 -2.2072749 -0.51676030
## X290 0.88951564 -1.5606477 -0.15609111
## X291 1.60225879 -1.3093333 0.36643191
## X292 2.10104410 -1.1711830 0.44948974
## X294 0.64279595 -1.7719568 -0.37519232
## X297 1.05460735 -1.6607312 0.44948974
## X298 0.70781531 -1.7147984 -0.51676030
## X299 1.34711282 -1.1394343 0.28035085
## X301 0.94620673 -1.8325815 -0.06092806
## X302 0.35404039 -1.7147984 -0.42519843
## X303 1.47863123 -1.6094379 0.52982213
## X304 2.37256620 -1.1086626 0.68328157
## X305 1.39190522 -1.2378744 0.75680975
## X306 1.56185602 -1.1711830 0.44948974
## X307 2.06885532 -1.1086626 0.82842712
## X308 0.70781531 -1.5606477 -0.92296704
## X311 0.27296583 -2.1202635 0.09761770
## X312 1.68082597 -0.8439701 0.60768096
## X313 1.00119771 -0.9162907 -0.09212160
## X314 1.56185602 -1.3470736 0.60768096
## X315 0.18739989 -1.6094379 0.52982213
## X316 1.86560359 -1.7719568 0.36643191
## X317 1.52065981 -1.4696760 0.19089023
## X320 0.70781531 -0.9416085 0.09761770
## X321 0.50475301 -1.4271164 -0.67335008
## X322 2.42897180 -1.8325815 0.52982213
## X323 1.86560359 -1.1394343 0.44948974
## X324 1.20635622 -1.6094379 0.28035085
## X325 0.94620673 -1.7719568 0.44948974
## X326 1.25439977 -1.4271164 0.19089023
## X327 1.64190425 -1.5141277 0.52982213
## X329 1.71905522 -1.6607312 0.82842712
## X330 1.39190522 -1.5141277 -0.14527630
## X331 1.00119771 -1.3470736 -0.01002513
## X332 0.94620673 -1.7719568 0.00000000
## X333 2.42897180 -1.4696760 1.40587727
## Adiponectin Alpha_1_Antichymotrypsin Alpha_1_Antitrypsin
## X1 -5.360193 1.7404662 -12.631361
## X2 -5.020686 1.4586150 -11.909882
## X3 -5.809143 1.1939225 -13.642963
## X5 -4.779524 2.1282317 -11.133063
## X6 -5.221356 1.3083328 -12.134638
## X7 -6.119298 0.8329091 -12.813142
## X8 -4.879607 1.5260563 -13.310348
## X9 -5.167289 0.7419373 -12.907477
## X11 -4.840893 1.0986123 -13.310348
## X12 -4.199705 1.9021075 -11.838035
## X14 -5.776353 1.3862944 -11.909882
## X16 -4.199705 1.4109870 -11.983227
## X17 -4.699481 1.8245493 -11.499497
## X18 -6.265901 1.2237754 -14.135373
## X19 -4.422849 1.3083328 -12.292758
## X20 -5.132803 2.1633230 -8.932463
## X21 -5.683980 0.9555114 -14.135373
## X22 -5.914504 1.3609766 -15.344812
## X23 -6.377127 1.1314021 -13.642963
## X24 -5.843045 1.3083328 -13.310348
## X25 -4.919881 0.7884574 -13.528896
## X26 -5.099467 1.0986123 -13.205557
## X28 -6.502290 1.5260563 -11.909882
## X29 -6.165818 0.9932518 -14.135373
## X30 -5.278515 1.1314021 -13.760451
## X31 -5.099467 1.5475625 -12.058126
## X34 -5.426151 1.4109870 -11.435607
## X35 -4.199705 1.2237754 -12.458129
## X36 -3.963316 2.1041342 -11.909882
## X37 -4.767689 1.1314021 -14.548755
## X38 -5.005648 1.3609766 -13.528896
## X39 -5.843045 0.8754687 -16.321511
## X40 -5.991465 1.7749524 -13.418078
## X41 -3.649659 1.8870696 -8.191715
## X42 -4.605170 1.1314021 -13.881545
## X43 -4.688552 1.0647107 -13.004247
## X44 -5.259097 1.5892352 -12.907477
## X45 -6.502290 1.8082888 -12.058126
## X46 -6.214608 1.3862944 -15.008176
## X47 -4.605170 1.3862944 -11.630963
## X48 -5.099467 1.4816045 -15.344812
## X50 -5.184989 1.3862944 -11.698625
## X51 -4.677741 1.3609766 -12.374500
## X53 -5.083206 1.5892352 -13.881545
## X55 -5.496768 1.3862944 -15.344812
## X56 -4.509860 1.5260563 -11.698625
## X57 -5.403678 1.8405496 -12.212827
## X59 -4.509860 1.6486586 -11.250842
## X60 -5.318520 1.4586150 -13.103567
## X61 -5.776353 1.1631508 -14.006447
## X62 -5.914504 0.8329091 -15.008176
## X63 -5.521461 1.3350011 -12.907477
## X64 -4.744432 1.7749524 -12.631361
## X65 -5.546779 0.7419373 -15.523564
## X67 -4.990833 1.4350845 -14.406260
## X68 -4.199705 1.3862944 -14.268559
## X69 -5.683980 0.8329091 -15.008176
## X70 -6.165818 0.9932518 -14.406260
## X71 -6.032287 1.7047481 -12.134638
## X72 -3.540459 2.2082744 -10.963846
## X73 -5.067206 1.5892352 -14.135373
## X74 -4.615221 1.4109870 -10.802885
## X75 -4.342806 2.0412203 -12.907477
## X76 -5.426151 1.5260563 -10.363053
## X77 -3.912023 1.6292405 -11.564602
## X78 -4.803621 1.5892352 -11.311317
## X80 -6.214608 1.4350845 -13.205557
## X81 -5.572754 1.3862944 -12.292758
## X82 -5.067206 1.1939225 -12.292758
## X83 -4.828314 1.1631508 -12.374500
## X84 -5.099467 1.2809338 -11.564602
## X85 -5.713833 1.5686159 -12.212827
## X86 -4.509860 2.1633230 -11.019298
## X88 -4.866535 1.4350845 -13.418078
## X90 -5.878136 0.9932518 -14.849365
## X93 -4.342806 1.8082888 -13.004247
## X94 -4.199705 2.2823824 -12.292758
## X95 -4.919881 1.2527630 -12.058126
## X96 -5.991465 1.0986123 -14.406260
## X97 -5.360193 0.9162907 -13.881545
## X98 -5.914504 0.9932518 -15.344812
## X99 -5.403678 1.5040774 -14.268559
## X100 -5.278515 1.1939225 -15.008176
## X103 -5.221356 1.3609766 -13.310348
## X104 -5.167289 1.2237754 -13.881545
## X105 -5.115996 1.1939225 -12.374500
## X107 -5.599422 1.2809338 -13.004247
## X108 -5.005648 1.7047481 -13.103567
## X109 -5.809143 0.4054651 -16.780588
## X110 -5.952244 0.7884574 -15.173178
## X111 -4.744432 1.2527630 -14.268559
## X112 -3.963316 1.9169226 -12.058126
## X113 -4.342806 1.8718022 -11.191436
## X114 -6.119298 1.2237754 -13.418078
## X115 -4.268698 1.7227666 -11.499497
## X117 -5.991465 1.2237754 -14.006447
## X118 -5.572754 1.4350845 -13.881545
## X121 -4.135167 1.9315214 -9.562842
## X123 -5.776353 0.9555114 -13.103567
## X124 -5.020686 1.6292405 -13.004247
## X126 -5.298317 0.9932518 -13.418078
## X128 -3.912023 1.9169226 -10.750945
## X129 -5.221356 1.1939225 -13.103567
## X130 -5.051457 1.4350845 -10.363053
## X131 -5.744604 1.0647107 -13.642963
## X132 -5.654992 1.7578579 -12.058126
## X133 -4.342806 1.7047481 -11.983227
## X134 -4.906275 1.6677068 -12.813142
## X135 -5.744604 1.4816045 -11.499497
## X136 -4.074542 1.4350845 -12.374500
## X137 -6.319969 0.9162907 -14.135373
## X139 -5.683980 1.0986123 -14.406260
## X140 -5.521461 1.2237754 -13.881545
## X141 -5.240048 1.4109870 -11.250842
## X143 -4.947660 1.5260563 -12.134638
## X144 -5.426151 1.4816045 -13.528896
## X145 -5.914504 0.7884574 -14.696346
## X146 -5.099467 1.7404662 -13.103567
## X147 -4.947660 1.5040774 -13.881545
## X148 -4.199705 2.2512918 -12.907477
## X149 -5.360193 0.6931472 -15.008176
## X152 -5.496768 1.8082888 -12.458129
## X153 -6.319969 1.6094379 -12.374500
## X154 -5.099467 0.9162907 -14.696346
## X155 -5.115996 1.1314021 -12.631361
## X156 -5.132803 1.7227666 -12.543721
## X157 -5.259097 1.2527630 -13.310348
## X158 -5.991465 1.0647107 -14.268559
## X159 -5.184989 1.6292405 -11.838035
## X160 -4.828314 1.7404662 -12.721137
## X161 -4.509860 1.3609766 -12.458129
## X162 -5.472671 1.2237754 -15.523564
## X163 -5.240048 0.6931472 -15.709974
## X165 -5.496768 1.0986123 -14.135373
## X166 -5.318520 0.7419373 -14.135373
## X167 -4.755993 1.0647107 -14.406260
## X168 -5.449140 1.4816045 -14.006447
## X169 -5.203007 1.0647107 -13.205557
## X170 -4.268698 1.1314021 -12.134638
## X171 -5.713833 1.5040774 -10.599937
## X172 -5.654992 1.0296194 -12.631361
## X174 -3.575551 1.6292405 -11.435607
## X175 -4.828314 1.9021075 -11.838035
## X176 -5.020686 1.6863990 -11.133063
## X177 -5.167289 1.4586150 -14.548755
## X178 -5.240048 0.9555114 -14.135373
## X179 -3.506558 1.9021075 -13.103567
## X180 -4.853632 1.6292405 -13.103567
## X181 -6.074846 0.6931472 -15.173178
## X182 -5.521461 1.4109870 -15.173178
## X183 -4.892852 1.3862944 -13.205557
## X184 -5.654992 1.2809338 -12.813142
## X185 -4.422849 1.4109870 -12.212827
## X186 -5.496768 1.0647107 -16.545310
## X189 -5.991465 0.9932518 -13.205557
## X190 -5.278515 1.6292405 -14.406260
## X191 -5.449140 1.4109870 -13.528896
## X192 -5.149897 1.5040774 -13.881545
## X193 -4.645992 1.7749524 -11.191436
## X194 -4.135167 1.2237754 -12.907477
## X195 -5.546779 0.9932518 -12.907477
## X197 -5.035953 1.5040774 -11.983227
## X198 -4.947660 0.8329091 -15.904641
## X200 -5.952244 1.1939225 -12.721137
## X201 -5.496768 1.0986123 -13.642963
## X202 -6.377127 0.8329091 -14.696346
## X205 -5.403678 1.3609766 -17.028429
## X208 -5.203007 1.4816045 -13.881545
## X210 -4.605170 1.5040774 -10.699822
## X212 -5.083206 1.6677068 -12.631361
## X213 -5.115996 1.4109870 -12.458129
## X214 -6.119298 1.6863990 -12.543721
## X215 -5.991465 1.1631508 -14.696346
## X216 -4.422849 1.5475625 -13.004247
## X218 -4.605170 1.7047481 -13.103567
## X219 -5.259097 0.8754687 -13.760451
## X220 -6.725434 1.0986123 -14.406260
## X223 -5.496768 1.3862944 -12.212827
## X224 -6.437752 1.2809338 -12.721137
## X225 -5.521461 1.2809338 -13.528896
## X226 -4.906275 1.6486586 -13.205557
## X227 -4.767689 2.0541237 -12.813142
## X228 -6.265901 1.0647107 -14.696346
## X229 -6.119298 1.1939225 -14.006447
## X230 -4.744432 1.4350845 -12.292758
## X231 -6.074846 1.0647107 -11.767633
## X232 -5.259097 1.2809338 -12.907477
## X233 -5.020686 1.2809338 -13.205557
## X234 -4.268698 1.1939225 -11.983227
## X236 -5.991465 0.7419373 -13.004247
## X237 -4.710531 1.7047481 -10.185537
## X239 -5.914504 1.0296194 -13.418078
## X240 -5.449140 0.9162907 -14.548755
## X241 -6.725434 1.1939225 -11.983227
## X242 -3.575551 1.6781472 -14.135373
## X243 -5.184989 1.7227666 -10.317725
## X244 -4.906275 1.4109870 -11.698625
## X245 -6.319969 0.4054651 -15.904641
## X246 -4.677741 1.5892352 -11.698625
## X247 -5.744604 0.5877867 -15.709974
## X249 -5.339139 1.2237754 -14.849365
## X250 -5.683980 1.1631508 -13.310348
## X251 -4.947660 0.8754687 -12.721137
## X253 -4.721704 1.8718022 -10.750945
## X254 -5.083206 1.7578579 -12.721137
## X255 -4.509860 1.7749524 -11.191436
## X256 -5.654992 1.1314021 -15.523564
## X257 -5.184989 0.6418539 -15.523564
## X258 -4.017384 1.2809338 -10.802885
## X260 -5.020686 1.1939225 -13.310348
## X261 -5.005648 0.7884574 -13.004247
## X262 -4.947660 1.1314021 -14.135373
## X263 -4.840893 1.5040774 -13.004247
## X264 -4.342806 2.3025851 -8.191715
## X265 -5.654992 1.2809338 -14.406260
## X267 -5.449140 1.3350011 -13.310348
## X268 -4.268698 1.6486586 -13.103567
## X269 -6.725434 0.9162907 -14.696346
## X270 -5.472671 1.3609766 -14.268559
## X271 -4.677741 1.3083328 -12.458129
## X272 -4.779524 1.5260563 -11.564602
## X273 -5.278515 1.4586150 -13.881545
## X274 -5.381699 0.2623643 -13.418078
## X275 -5.952244 1.5260563 -13.205557
## X277 -5.184989 1.4586150 -11.838035
## X278 -6.377127 1.4350845 -12.292758
## X279 -5.843045 1.3862944 -12.058126
## X281 -5.259097 1.1314021 -13.205557
## X282 -5.654992 1.5892352 -12.458129
## X283 -3.649659 1.3609766 -12.907477
## X287 -5.259097 1.3350011 -14.548755
## X289 -5.914504 1.0986123 -13.004247
## X290 -5.952244 1.3609766 -14.268559
## X291 -5.240048 1.4586150 -12.721137
## X292 -6.377127 1.3083328 -12.374500
## X294 -6.119298 1.0296194 -15.709974
## X297 -4.961845 1.3609766 -14.006447
## X298 -6.319969 0.8754687 -13.205557
## X299 -5.403678 0.8754687 -12.721137
## X301 -6.165818 0.8754687 -12.721137
## X302 -5.259097 1.0986123 -11.311317
## X303 -4.840893 1.3083328 -13.310348
## X304 -4.422849 1.7047481 -11.499497
## X305 -4.840893 1.5475625 -15.904641
## X306 -4.866535 1.4586150 -11.133063
## X307 -3.963316 2.1747517 -11.191436
## X308 -4.721704 1.7404662 -12.458129
## X311 -4.422849 1.5475625 -11.019298
## X312 -5.051457 1.4109870 -8.417032
## X313 -4.853632 1.3083328 -14.406260
## X314 -5.776353 0.7884574 -15.523564
## X315 -5.035953 2.0014800 -12.721137
## X316 -5.132803 1.0296194 -13.310348
## X317 -5.843045 1.3350011 -13.418078
## X320 -4.677741 1.6863990 -11.630963
## X321 -4.509860 1.1939225 -12.292758
## X322 -4.135167 2.1162555 -10.551134
## X323 -4.074542 1.7227666 -13.418078
## X324 -6.502290 0.9162907 -14.849365
## X325 -5.339139 1.6677068 -12.907477
## X326 -4.422849 1.4586150 -14.006447
## X327 -4.779524 1.4586150 -11.838035
## X329 -5.449140 1.0986123 -16.321511
## X330 -4.906275 1.6094379 -11.838035
## X331 -4.509860 1.1939225 -14.406260
## X332 -5.521461 1.7047481 -12.543721
## X333 -5.051457 1.2809338 -12.907477
## Alpha_1_Microglobulin Alpha_2_Macroglobulin Angiopoietin_2_ANG_2
## X1 -2.577022 -72.65029 1.06471074
## X2 -3.244194 -154.61228 0.74193734
## X3 -2.882404 -136.52918 0.83290912
## X5 -2.343407 -144.94460 0.95551145
## X6 -2.551046 -154.61228 -0.05129329
## X7 -3.270169 -149.60441 0.78845736
## X8 -2.900422 -144.94460 0.26236426
## X9 -3.649659 -194.94684 0.64185389
## X11 -3.079114 -91.36978 0.83290912
## X12 -2.353878 -132.71508 0.26236426
## X14 -2.513306 -104.44595 0.64185389
## X16 -2.900422 -94.72274 1.30833282
## X17 -2.733368 -149.60441 0.83290912
## X18 -3.296837 -225.75583 0.26236426
## X19 -2.975930 -179.08749 0.47000363
## X20 -2.590267 -186.64150 0.58778666
## X21 -2.937463 -149.60441 0.53062825
## X22 -3.688879 -165.84824 0.91629073
## X23 -3.575551 -238.63748 0.09531018
## X24 -3.411248 -179.08749 1.06471074
## X25 -3.170086 -194.94684 0.53062825
## X26 -3.218876 -186.64150 0.69314718
## X28 -3.057608 -165.84824 0.64185389
## X29 -3.816713 -238.63748 0.47000363
## X30 -3.270169 -225.75583 0.33647224
## X31 -2.617296 -172.18413 0.87546874
## X34 -2.563950 -186.64150 0.64185389
## X35 -2.453408 -179.08749 0.26236426
## X36 -2.040221 -140.59662 0.83290912
## X37 -3.324236 -172.18413 0.78845736
## X38 -3.015935 -154.61228 0.64185389
## X39 -3.194183 -144.94460 0.69314718
## X40 -2.796881 -140.59662 0.64185389
## X41 -2.501036 -106.66533 0.53062825
## X42 -2.796881 -125.75495 0.87546874
## X43 -3.575551 -194.94684 0.26236426
## X44 -2.645075 -89.78989 0.78845736
## X45 -2.748872 -136.52918 1.09861229
## X46 -3.270169 -125.75495 1.02961942
## X47 -3.079114 -100.30070 0.40546511
## X48 -3.057608 -71.69519 1.09861229
## X50 -2.590267 -93.01273 1.13140211
## X51 -3.270169 -125.75495 1.25276297
## X53 -2.918771 -82.71675 0.58778666
## X55 -2.918771 -160.01040 0.40546511
## X56 -2.419119 -140.59662 0.40546511
## X57 -2.525729 -67.30674 1.19392247
## X59 -2.441847 -108.99239 0.78845736
## X60 -3.123566 -84.03131 0.74193734
## X61 -3.411248 -179.08749 0.64185389
## X62 -3.772261 -225.75583 0.33647224
## X63 -2.780621 -96.50416 0.78845736
## X64 -2.453408 -165.84824 0.58778666
## X65 -3.352407 -160.01040 0.40546511
## X67 -3.324236 -122.56978 1.30833282
## X68 -2.659260 -75.69273 0.83290912
## X69 -3.473768 -122.56978 0.09531018
## X70 -3.729701 -149.60441 0.83290912
## X71 -2.441847 -179.08749 0.64185389
## X72 -1.832581 -138.25835 1.48160454
## X73 -1.897120 -81.44692 1.13140211
## X74 -2.813411 -186.64150 0.18232156
## X75 -2.419119 -129.13061 0.40546511
## X76 -2.718101 -154.61228 0.64185389
## X77 -2.688248 -165.84824 0.58778666
## X78 -2.353878 -140.59662 1.16315081
## X80 -2.830218 -91.36978 0.47000363
## X81 -3.170086 -160.01040 0.83290912
## X82 -3.015935 -149.60441 0.83290912
## X83 -2.673649 -165.84824 0.33647224
## X84 -3.146555 -154.61228 0.47000363
## X85 -2.590267 -136.52918 0.95551145
## X86 -1.832581 -140.59662 0.95551145
## X88 -2.441847 -74.64766 0.87546874
## X90 -4.342806 -253.28958 0.33647224
## X93 -2.780621 -140.59662 1.16315081
## X94 -2.333044 -102.32669 1.41098697
## X95 -3.244194 -214.33276 0.18232156
## X96 -3.015935 -172.18413 0.74193734
## X97 -3.170086 -172.18413 1.09861229
## X98 -3.816713 -186.64150 0.53062825
## X99 -2.703063 -140.59662 0.74193734
## X100 -3.170086 -154.61228 0.87546874
## X103 -3.218876 -116.70786 1.16315081
## X104 -3.194183 -154.61228 0.91629073
## X105 -3.057608 -172.18413 1.02961942
## X107 -2.813411 -93.01273 0.87546874
## X108 -2.353878 -165.84824 0.83290912
## X109 -3.688879 -204.12656 0.09531018
## X110 -4.017384 -225.75583 0.33647224
## X111 -2.956512 -102.32669 0.95551145
## X112 -1.966113 -93.01273 0.91629073
## X113 -2.419119 -102.32669 0.69314718
## X114 -3.296837 -194.94684 -0.54472718
## X115 -2.718101 -104.44595 0.47000363
## X117 -3.688879 -253.28958 0.95551145
## X118 -2.937463 -100.30070 1.19392247
## X121 -2.631089 -116.70786 0.91629073
## X123 -3.649659 -225.75583 0.33647224
## X124 -2.375156 -160.01040 0.83290912
## X126 -3.324236 -165.84824 0.78845736
## X128 -2.590267 -132.71508 0.69314718
## X129 -3.079114 -194.94684 0.40546511
## X130 -2.302585 -144.94460 0.91629073
## X131 -3.079114 -186.64150 0.53062825
## X132 -2.733368 -204.12656 0.78845736
## X133 -2.353878 -149.60441 0.83290912
## X134 -2.441847 -94.72274 0.69314718
## X135 -3.079114 -119.55888 1.16315081
## X136 -3.015935 -165.84824 0.53062825
## X137 -3.575551 -194.94684 0.18232156
## X139 -3.244194 -172.18413 0.47000363
## X140 -2.830218 -108.99239 0.64185389
## X141 -2.956512 -165.84824 1.02961942
## X143 -2.900422 -204.12656 0.53062825
## X144 -2.659260 -165.84824 0.40546511
## X145 -3.688879 -225.75583 0.33647224
## X146 -2.120264 -140.59662 0.87546874
## X147 -2.995732 -89.78989 0.64185389
## X148 -2.040221 -136.52918 0.69314718
## X149 -3.057608 -179.08749 0.47000363
## X152 -2.302585 -59.45638 1.33500107
## X153 -2.577022 -116.70786 0.64185389
## X154 -3.244194 -172.18413 0.47000363
## X155 -3.270169 -160.01040 0.33647224
## X156 -2.385967 -165.84824 0.58778666
## X157 -3.296837 -165.84824 0.87546874
## X158 -3.381395 -194.94684 0.53062825
## X159 -3.506558 -194.94684 0.18232156
## X160 -2.882404 -136.52918 0.69314718
## X161 -2.302585 -149.60441 0.91629073
## X162 -3.729701 -119.55888 0.87546874
## X163 -4.017384 -194.94684 0.26236426
## X165 -3.352407 -154.61228 0.78845736
## X166 -3.352407 -149.60441 0.53062825
## X167 -2.813411 -140.59662 1.33500107
## X168 -3.244194 -186.64150 0.26236426
## X169 -3.146555 -194.94684 0.40546511
## X170 -2.995732 -186.64150 0.40546511
## X171 -2.780621 -154.61228 0.74193734
## X172 -3.411248 -225.75583 0.47000363
## X174 -1.897120 -71.69519 0.26236426
## X175 -2.343407 -119.55888 0.64185389
## X176 -2.688248 -125.75495 0.58778666
## X177 -3.540459 -165.84824 0.87546874
## X178 -3.411248 -225.75583 0.33647224
## X179 -2.120264 -116.70786 0.53062825
## X180 -2.688248 -160.01040 0.64185389
## X181 -3.381395 -225.75583 0.26236426
## X182 -3.296837 -194.94684 0.91629073
## X183 -2.780621 -214.33276 0.53062825
## X184 -2.918771 -149.60441 0.95551145
## X185 -2.476938 -144.94460 0.64185389
## X186 -3.244194 -194.94684 0.53062825
## X189 -3.649659 -186.64150 0.78845736
## X190 -2.937463 -132.71508 0.87546874
## X191 -3.352407 -179.08749 0.78845736
## X192 -2.631089 -149.60441 1.06471074
## X193 -2.796881 -140.59662 0.99325177
## X194 -2.847312 -160.01040 0.18232156
## X195 -3.101093 -172.18413 0.58778666
## X197 -2.120264 -94.72274 0.64185389
## X198 -3.611918 -186.64150 0.40546511
## X200 -3.381395 -165.84824 0.53062825
## X201 -3.411248 -144.94460 0.58778666
## X202 -3.688879 -225.75583 0.40546511
## X205 -2.673649 -172.18413 0.53062825
## X208 -2.918771 -149.60441 1.09861229
## X210 -2.501036 -98.36175 0.83290912
## X212 -2.748872 -186.64150 0.58778666
## X213 -2.453408 -179.08749 0.58778666
## X214 -2.796881 -160.01040 0.78845736
## X215 -3.611918 -136.52918 0.69314718
## X216 -2.780621 -194.94684 0.33647224
## X218 -2.207275 -165.84824 0.58778666
## X219 -3.270169 -179.08749 0.69314718
## X220 -3.411248 -172.18413 0.69314718
## X223 -2.563950 -194.94684 0.47000363
## X224 -3.244194 -214.33276 0.33647224
## X225 -3.473768 -179.08749 1.16315081
## X226 -3.244194 -136.52918 1.52605630
## X227 -1.966113 -179.08749 0.40546511
## X228 -3.352407 -214.33276 0.40546511
## X229 -3.170086 -225.75583 0.18232156
## X230 -1.966113 -75.69273 0.99325177
## X231 -3.123566 -144.94460 0.47000363
## X232 -2.975930 -140.59662 0.53062825
## X233 -3.036554 -186.64150 0.18232156
## X234 -2.673649 -129.13061 0.53062825
## X236 -3.352407 -204.12656 0.58778666
## X237 -2.563950 -149.60441 0.09531018
## X239 -3.863233 -253.28958 0.18232156
## X240 -2.617296 -116.70786 0.47000363
## X241 -3.244194 -179.08749 0.53062825
## X242 -1.771957 -160.01040 0.18232156
## X243 -2.733368 -179.08749 0.40546511
## X244 -2.673649 -172.18413 0.64185389
## X245 -3.912023 -253.28958 0.09531018
## X246 -2.322788 -149.60441 0.74193734
## X247 -3.729701 -204.12656 0.47000363
## X249 -2.995732 -160.01040 0.95551145
## X250 -2.813411 -179.08749 0.74193734
## X251 -3.218876 -179.08749 0.58778666
## X253 -2.525729 -144.94460 0.87546874
## X254 -2.430418 -132.71508 1.36097655
## X255 -2.617296 -106.66533 1.02961942
## X256 -3.611918 -186.64150 0.58778666
## X257 -3.218876 -214.33276 0.40546511
## X258 -2.302585 -160.01040 1.28093385
## X260 -2.900422 -179.08749 0.69314718
## X261 -3.270169 -214.33276 0.33647224
## X262 -3.411248 -194.94684 0.53062825
## X263 -2.796881 -104.44595 0.91629073
## X264 -2.353878 -144.94460 0.91629073
## X265 -3.352407 -186.64150 0.74193734
## X267 -2.813411 -160.01040 1.16315081
## X268 -2.385967 -79.03231 0.69314718
## X269 -3.816713 -194.94684 0.69314718
## X270 -3.146555 -129.13061 1.13140211
## X271 -2.501036 -122.56978 0.99325177
## X272 -2.780621 -194.94684 0.78845736
## X273 -3.244194 -116.70786 0.83290912
## X274 -3.411248 -149.60441 0.58778666
## X275 -2.864704 -179.08749 0.91629073
## X277 -2.748872 -186.64150 0.53062825
## X278 -2.813411 -154.61228 0.78845736
## X279 -3.244194 -204.12656 0.53062825
## X281 -3.057608 -186.64150 0.83290912
## X282 -2.513306 -194.94684 0.58778666
## X283 -2.513306 -111.43549 1.41098697
## X287 -3.079114 -172.18413 0.78845736
## X289 -3.270169 -253.28958 0.33647224
## X290 -3.146555 -214.33276 0.09531018
## X291 -2.703063 -140.59662 0.33647224
## X292 -2.995732 -179.08749 1.25276297
## X294 -3.244194 -253.28958 0.64185389
## X297 -2.577022 -179.08749 0.58778666
## X298 -2.617296 -186.64150 0.83290912
## X299 -3.540459 -132.71508 1.02961942
## X301 -2.864704 -194.94684 0.64185389
## X302 -3.324236 -289.68493 0.00000000
## X303 -3.057608 -172.18413 0.78845736
## X304 -3.381395 -172.18413 0.78845736
## X305 -2.718101 -132.71508 1.16315081
## X306 -2.040221 -165.84824 0.91629073
## X307 -2.120264 -140.59662 0.69314718
## X308 -2.419119 -122.56978 0.64185389
## X311 -2.764621 -165.84824 0.33647224
## X312 -2.688248 -160.01040 1.19392247
## X313 -3.057608 -186.64150 0.83290912
## X314 -3.772261 -172.18413 0.69314718
## X315 -2.538307 -149.60441 0.53062825
## X316 -3.381395 -154.61228 0.53062825
## X317 -3.244194 -160.01040 0.53062825
## X320 -2.040221 -179.08749 0.26236426
## X321 -2.813411 -165.84824 -0.49429632
## X322 -2.120264 -154.61228 1.16315081
## X323 -2.430418 -119.55888 0.74193734
## X324 -3.649659 -125.75495 0.47000363
## X325 -2.813411 -140.59662 1.13140211
## X326 -3.123566 -165.84824 0.87546874
## X327 -2.419119 -86.80482 0.74193734
## X329 -3.324236 -160.01040 0.95551145
## X330 -2.120264 -154.61228 0.40546511
## X331 -3.170086 -179.08749 -0.24846136
## X332 -3.036554 -132.71508 0.40546511
## X333 -2.995732 -194.94684 1.25276297
## Angiotensinogen Apolipoprotein_A_IV Apolipoprotein_A1 Apolipoprotein_A2
## X1 2.510547 -1.4271164 -7.402052 -0.26136476
## X2 2.457283 -1.6607312 -7.047017 -0.86750057
## X3 1.976365 -1.6607312 -7.684284 -0.65392647
## X5 2.862219 -1.1711830 -6.725434 0.09531018
## X6 2.524026 -1.3862944 -7.402052 -0.27443685
## X7 2.106653 -2.0402208 -7.751725 -0.94160854
## X8 2.079076 -1.4271164 -6.948577 -0.16251893
## X9 2.131782 -2.3968958 -7.929407 -0.77652879
## X11 2.654255 -1.6607312 -7.250246 -0.75502258
## X12 2.289800 -1.7719568 -7.469874 -0.40047757
## X14 2.064231 -1.5606477 -7.208860 -0.43078292
## X16 2.466248 -2.3751558 -7.684284 -0.47803580
## X17 2.389150 -1.5606477 -6.907755 -0.08338161
## X18 2.214041 -2.4191189 -8.294050 -1.46967597
## X19 2.205050 -2.0402208 -7.323271 -0.41551544
## X20 2.195737 -1.7719568 -6.725434 -0.31471074
## X21 2.555806 -2.3227878 -7.621105 -1.23787436
## X22 2.262201 -2.3859667 -7.824046 -1.27296568
## X23 2.262201 -2.2072749 -7.435388 -1.20397280
## X24 2.308607 -2.3644605 -7.323271 -0.46203546
## X25 2.397445 -1.5606477 -7.505592 -1.13943428
## X26 2.276391 -2.3751558 -7.581100 -0.96758403
## X28 2.262201 -1.9661129 -7.435388 -0.91629073
## X29 2.376085 -2.7646206 -7.875339 -1.20397280
## X30 2.119499 -1.8325815 -7.293418 -0.73396918
## X31 2.500997 -1.3862944 -7.013116 -0.22314355
## X34 1.751632 -2.0402208 -7.505592 -0.17435339
## X35 2.195737 -1.6607312 -7.561682 -0.18632958
## X36 2.064231 -1.2378744 -6.725434 0.40546511
## X37 2.416882 -2.1202635 -7.542634 -1.07880966
## X38 2.472010 -2.3968958 -7.824046 -0.67334455
## X39 2.682856 -1.8325815 -7.875339 -0.89159812
## X40 2.466248 -1.6094379 -7.452482 -0.75502258
## X41 1.932789 -1.2729657 -6.571283 0.18232156
## X42 2.597766 -1.6094379 -7.799353 -0.79850770
## X43 2.186083 -2.0402208 -7.875339 -1.10866262
## X44 2.314560 -1.7147984 -7.070274 -0.57981850
## X45 2.239267 -1.7147984 -7.024289 -0.34249031
## X46 2.195737 -2.2072749 -7.986565 -1.27296568
## X47 2.326029 -1.9661129 -7.662778 -1.17118298
## X48 2.622996 -1.9661129 -7.600902 -1.23787436
## X50 2.734317 -1.6094379 -7.452482 -0.13926207
## X51 1.976365 -1.8971200 -7.130899 -0.63487827
## X53 2.496038 -1.1086626 -7.130899 -0.49429632
## X55 2.447901 -1.9661129 -7.323271 -0.65392647
## X56 1.882416 -1.2729657 -7.236259 -0.03045921
## X57 2.804296 -1.8325815 -7.600902 -1.20397280
## X59 1.955322 -1.5606477 -7.581100 -0.46203546
## X60 2.143544 -2.2072749 -7.929407 -0.96758403
## X61 2.624223 -1.7719568 -7.902008 -0.96758403
## X62 2.119499 -2.3434071 -7.849364 -0.99425227
## X63 2.014615 -2.0402208 -7.957577 -0.38566248
## X64 2.014615 -1.6094379 -7.106206 0.00000000
## X65 2.119499 -1.9661129 -8.145630 -1.56064775
## X67 2.320365 -1.9661129 -7.600902 -1.02165125
## X68 2.308607 -1.4271164 -7.195437 -0.40047757
## X69 2.239267 -2.3538784 -7.706263 -1.23787436
## X70 2.176062 -2.3025851 -7.728736 -1.30933332
## X71 2.064231 -1.8325815 -7.452482 -0.69314718
## X72 2.393337 -1.5141277 -6.319969 0.33647224
## X73 2.154822 -1.3862944 -7.452482 -0.69314718
## X74 2.079076 -1.4271164 -7.082109 -0.23572233
## X75 1.789161 -1.6607312 -6.812445 0.18232156
## X76 2.239267 -1.7719568 -7.662778 -0.34249031
## X77 2.472010 -1.7147984 -7.047017 -0.43078292
## X78 2.371551 -1.4271164 -7.418581 -0.57981850
## X80 2.431242 -1.6094379 -7.706263 -0.43078292
## X81 2.498534 -1.7147984 -7.751725 -0.73396918
## X82 2.460316 -1.8971200 -7.338538 -0.59783700
## X83 2.540605 -1.7147984 -7.308233 -0.21072103
## X84 2.064231 -1.9661129 -7.706263 -0.54472718
## X85 2.247148 -1.6094379 -7.487574 -0.47803580
## X86 2.629041 -1.3470736 -7.369791 -0.02020271
## X88 2.498534 -2.0402208 -7.751725 -0.84397007
## X90 1.854050 -2.7333680 -8.217089 -1.89711998
## X93 2.154822 -1.5606477 -7.435388 -0.59783700
## X94 2.557619 -1.3862944 -6.812445 -0.08338161
## X95 2.632564 -2.2072749 -7.236259 -0.89159812
## X96 2.736448 -2.0402208 -7.902008 -1.13943428
## X97 2.546456 -1.8325815 -7.775256 -1.23787436
## X98 2.490948 -2.1202635 -7.706263 -1.27296568
## X99 1.908566 -2.2072749 -7.775256 -1.04982212
## X100 2.619258 -1.8971200 -8.078938 -0.69314718
## X103 2.106653 -1.6607312 -7.452482 -0.82098055
## X104 2.079076 -1.9661129 -7.751725 -0.67334455
## X105 2.371551 -2.1202635 -7.130899 -0.69314718
## X107 2.262201 -1.6607312 -7.435388 -0.37106368
## X108 2.106653 -0.9416085 -6.907755 0.09531018
## X109 2.308607 -2.2072749 -7.957577 -1.30933332
## X110 2.320365 -2.2072749 -8.468403 -1.60943791
## X111 2.247148 -2.0402208 -7.385791 -0.51082562
## X112 2.342236 -0.9416085 -6.571283 0.00000000
## X113 2.320365 -1.7147984 -6.812445 0.33647224
## X114 2.326029 -2.3126354 -7.385791 -0.61618614
## X115 1.751632 -1.5141277 -7.130899 -0.09431068
## X117 2.431242 -2.3227878 -7.621105 -0.91629073
## X118 2.763185 -2.1202635 -7.728736 -1.04982212
## X121 1.882416 -1.8325815 -7.487574 -0.49429632
## X123 2.579644 -1.8325815 -7.929407 -1.07880966
## X124 2.532501 -1.7719568 -7.169120 -0.08338161
## X126 2.231132 -2.0402208 -7.929407 -0.96758403
## X128 2.633721 -1.5606477 -6.645391 0.26236426
## X129 1.996082 -2.0402208 -7.641724 -0.96758403
## X130 2.457283 -1.7147984 -7.169120 -0.40047757
## X131 2.176062 -2.2072749 -7.338538 -0.79850770
## X132 2.239267 -1.3862944 -7.581100 -0.41551544
## X133 2.424185 -1.1086626 -6.377127 0.18232156
## X134 2.457283 -1.7147984 -7.452482 -0.77652879
## X135 1.789161 -1.8325815 -7.278819 -0.79850770
## X136 2.231132 -1.7719568 -7.118476 -0.46203546
## X137 2.466248 -2.0402208 -7.902008 -1.27296568
## X139 2.735867 -2.0402208 -7.278819 -0.86750057
## X140 2.454203 -1.6607312 -7.728736 -0.75502258
## X141 2.165651 -2.0402208 -7.323271 -0.54472718
## X143 2.032084 -1.6094379 -7.208860 -0.51082562
## X144 2.463304 -1.8971200 -7.561682 -0.65392647
## X145 2.205050 -2.2072749 -8.180721 -1.42711636
## X146 1.996082 -1.3862944 -7.208860 -0.05129329
## X147 2.331559 -2.0402208 -7.929407 -1.07880966
## X148 2.119499 -1.0788097 -6.725434 0.26236426
## X149 2.480350 -1.8971200 -7.621105 -1.10866262
## X152 2.500997 -1.3862944 -6.812445 -0.26136476
## X153 2.014615 -2.2072749 -7.118476 0.00000000
## X154 2.505832 -1.9661129 -7.751725 -1.04982212
## X155 2.532501 -1.6607312 -7.542634 -0.84397007
## X156 2.048593 -1.4696760 -7.024289 -0.09431068
## X157 1.955322 -2.1202635 -7.902008 -0.96758403
## X158 2.296235 -2.3025851 -7.684284 -0.94160854
## X159 2.366924 -2.5639499 -8.047190 -1.10866262
## X160 2.550254 -1.8971200 -6.812445 -0.32850407
## X161 2.314560 -1.7719568 -7.469874 -0.40047757
## X162 2.881043 -2.5257286 -8.217089 -1.10866262
## X163 2.768766 -2.2072749 -8.180721 -1.20397280
## X165 2.405433 -1.8971200 -8.111728 -1.23787436
## X166 2.214041 -2.3025851 -8.254829 -1.02165125
## X167 2.389150 -2.0402208 -7.824046 -0.94160854
## X168 2.032084 -1.5141277 -6.812445 -0.35667494
## X169 2.604783 -1.6607312 -7.523941 -0.77652879
## X170 2.064231 -2.3025851 -7.323271 -0.47803580
## X171 2.711100 -1.7147984 -7.369791 -0.19845094
## X172 2.444675 -1.9661129 -7.143478 -0.77652879
## X174 2.165651 -0.9675840 -6.907755 0.00000000
## X175 2.661193 -1.7147984 -6.725434 -0.03045921
## X176 1.955322 -1.6607312 -7.418581 -0.52763274
## X177 2.186083 -2.4418472 -7.293418 -0.65392647
## X178 2.143544 -2.4889147 -7.957577 -0.91629073
## X179 2.106653 -1.3093333 -6.908737 0.64185389
## X180 2.483054 -1.5606477 -7.505592 -0.99425227
## X181 2.106653 -2.0402208 -8.016418 -1.07880966
## X182 2.262201 -1.9661129 -7.662778 -0.69314718
## X183 1.751632 -1.7719568 -7.182192 0.09531018
## X184 2.376085 -1.7719568 -7.385791 -0.73396918
## X185 2.014615 -1.5141277 -7.542634 -0.23572233
## X186 2.247148 -1.7719568 -8.047190 -1.02165125
## X189 2.517407 -2.3859667 -8.217089 -1.38629436
## X190 2.397445 -1.8971200 -7.542634 -0.99425227
## X191 2.222729 -2.3025851 -7.849364 -0.79850770
## X192 2.154822 -2.0402208 -7.369791 -0.71334989
## X193 2.214041 -1.7719568 -7.684284 -0.96758403
## X194 2.666903 -1.4271164 -7.182192 -0.75502258
## X195 2.544526 -1.7719568 -7.338538 -0.61618614
## X197 2.480350 -1.3862944 -7.222466 -0.18632958
## X198 2.447901 -2.3126354 -7.662778 -0.89159812
## X200 2.801517 -2.3751558 -7.875339 -1.02165125
## X201 2.474829 -1.7719568 -7.402052 -0.65392647
## X202 2.747573 -2.1202635 -8.078938 -1.20397280
## X205 2.444675 -1.6607312 -7.354042 -0.73396918
## X208 1.908566 -1.7147984 -7.013116 -0.08338161
## X210 2.515148 -1.3862944 -6.959049 -0.34249031
## X212 1.882416 -1.5606477 -7.902008 -0.73396918
## X213 2.401476 -1.7719568 -7.323271 -0.16251893
## X214 1.996082 -1.8325815 -7.621105 -1.07880966
## X215 2.493510 -1.7719568 -8.145630 -1.42711636
## X216 2.143544 -1.4696760 -7.308233 -0.10536052
## X218 2.405433 -1.3862944 -7.156217 0.33647224
## X219 2.638269 -1.8971200 -7.684284 -0.84397007
## X220 2.308607 -2.4304185 -8.622554 -0.94160854
## X223 2.431242 -1.4271164 -7.338538 -0.35667494
## X224 1.908566 -1.7147984 -7.418581 -1.04982212
## X225 2.629041 -2.4079456 -7.957577 -1.20397280
## X226 2.093196 -2.0402208 -7.600902 -1.10866262
## X227 2.331559 -1.4271164 -6.645391 0.58778666
## X228 2.186083 -2.1202635 -8.334872 -1.46967597
## X229 2.538612 -2.2072749 -8.217089 -0.84397007
## X230 2.079076 -1.5141277 -7.222466 -0.54472718
## X231 2.532501 -2.4651040 -7.354042 -0.54472718
## X232 2.352437 -1.6094379 -7.561682 -0.49429632
## X233 1.882416 -1.7719568 -7.621105 -0.59783700
## X234 2.254788 -1.7147984 -7.047017 -0.24846136
## X236 2.610193 -2.6882476 -8.468403 -1.51412773
## X237 2.434683 -1.5141277 -7.278819 -0.24846136
## X239 1.882416 -2.3644605 -8.016418 -1.77195684
## X240 2.231132 -1.7147984 -7.902008 -1.23787436
## X241 2.079076 -2.5510465 -7.013116 -0.24846136
## X242 2.079076 -0.8209806 -6.165818 0.95551145
## X243 1.908566 -1.5141277 -7.684284 -0.46203546
## X244 2.380527 -2.0402208 -7.641724 -0.71334989
## X245 2.397445 -2.9565116 -8.568486 -1.42711636
## X246 2.578045 -1.2039728 -6.907755 -0.13926207
## X247 2.296235 -2.9004221 -8.421883 -1.34707365
## X249 2.239267 -2.2072749 -8.421883 -1.34707365
## X250 2.048593 -1.8325815 -7.402052 -0.56211892
## X251 1.823113 -2.2072749 -7.986565 -1.27296568
## X253 2.331559 -2.0402208 -7.058578 -0.71334989
## X254 2.413134 -1.8971200 -6.959049 -0.37106368
## X255 2.741567 -1.7147984 -7.236259 -0.06187540
## X256 2.384881 -2.2072749 -8.334872 -1.56064775
## X257 2.836869 -2.0402208 -7.986565 -1.07880966
## X258 2.176062 -1.1711830 -7.208860 0.18232156
## X260 2.296235 -1.8325815 -7.250246 0.09531018
## X261 2.389150 -2.0402208 -8.016418 -0.82098055
## X262 2.427744 -2.0402208 -7.505592 -0.67334455
## X263 2.485721 -2.0402208 -7.222466 -0.41551544
## X264 2.154822 -1.3470736 -6.969631 -0.17435339
## X265 2.262201 -2.2072749 -7.452482 -0.75502258
## X267 2.336959 -2.1202635 -7.600902 -0.91629073
## X268 2.457283 -1.5141277 -7.169120 -0.77652879
## X269 2.262201 -2.4304185 -8.334872 -1.27296568
## X270 1.996082 -2.2072749 -7.902008 -1.23787436
## X271 2.736448 -1.8971200 -7.264430 -0.46203546
## X272 2.262201 -2.3751558 -7.156217 -0.17435339
## X273 2.582800 -1.9661129 -7.849364 -0.96758403
## X274 2.384881 -2.3227878 -7.505592 -0.96758403
## X275 2.780924 -2.0402208 -7.561682 -0.17435339
## X277 2.032084 -1.6607312 -6.991137 0.09531018
## X278 2.131782 -1.8971200 -7.621105 -0.67334455
## X279 2.413134 -1.9661129 -7.600902 -0.73396918
## X281 2.427744 -1.8325815 -7.487574 -0.82098055
## X282 2.106653 -1.4271164 -7.047017 -0.31471074
## X283 2.512862 -1.6607312 -7.156217 -0.63487827
## X287 2.106653 -1.7719568 -7.208860 -0.54472718
## X289 2.032084 -1.7147984 -7.929407 -0.75502258
## X290 2.247148 -2.0402208 -7.469874 -1.02165125
## X291 2.553974 -1.8325815 -7.369791 -0.79850770
## X292 2.731131 -2.0402208 -7.581100 -0.57981850
## X294 1.996082 -2.1202635 -7.523941 -0.75502258
## X297 2.342236 -1.7719568 -7.799353 -0.71334989
## X298 1.955322 -1.6607312 -7.385791 -0.43078292
## X299 2.624223 -2.4079456 -7.600902 -1.23787436
## X301 2.079076 -2.0402208 -8.180721 -0.84397007
## X302 1.955322 -2.1202635 -7.542634 -0.43078292
## X303 2.205050 -1.7719568 -7.354042 -0.10536052
## X304 2.669681 -1.7147984 -7.902008 -1.30933332
## X305 2.573156 -1.8971200 -7.070274 -0.54472718
## X306 2.789726 -1.3862944 -7.222466 0.18232156
## X307 2.761752 -1.1394343 -7.024289 -0.08338161
## X308 2.463304 -1.0498221 -7.250246 0.09531018
## X311 1.932789 -1.7719568 -7.118476 -0.08338161
## X312 2.734317 -1.6094379 -7.264430 -0.69314718
## X313 2.822737 -1.7147984 -7.070274 -0.27443685
## X314 2.561191 -2.5133061 -7.849364 -1.20397280
## X315 2.362198 -1.1711830 -6.725434 0.40546511
## X316 1.976365 -1.8971200 -7.469874 -0.91629073
## X317 2.262201 -2.1202635 -7.418581 -0.86750057
## X320 2.792780 -1.0498221 -6.377127 0.47000363
## X321 2.331559 -1.4271164 -6.959049 0.26236426
## X322 2.231132 -1.4696760 -7.236259 -0.11653382
## X323 2.649104 -2.1202635 -7.435388 -0.56211892
## X324 2.376085 -2.6882476 -8.679712 -1.77195684
## X325 2.032084 -2.1202635 -7.581100 -0.34249031
## X326 2.438068 -2.0402208 -7.957577 -1.56064775
## X327 2.314560 -1.5606477 -7.236259 -0.79850770
## X329 1.908566 -1.9661129 -8.047190 -0.75502258
## X330 1.789161 -0.7765288 -6.645391 0.74193734
## X331 2.596327 -1.8971200 -7.469874 -0.56211892
## X332 2.289800 -2.0402208 -7.581100 -0.57981850
## X333 2.498534 -1.7719568 -7.195437 -0.49429632
## Apolipoprotein_B Apolipoprotein_CI Apolipoprotein_CIII Apolipoprotein_D
## X1 -4.624044 -1.2729657 -2.312635 2.0794415
## X2 -6.747507 -1.2729657 -2.343407 1.3350011
## X3 -3.976069 -1.7147984 -2.748872 1.3350011
## X5 -3.378594 -0.7550226 -1.514128 1.6292405
## X6 -2.963532 -1.6607312 -2.312635 1.9169226
## X7 -7.288830 -1.6607312 -2.375156 1.5260563
## X8 -3.888287 -0.9675840 -2.120264 1.7227666
## X9 -5.941894 -1.7719568 -2.476938 0.9555114
## X11 -5.702912 -1.5141277 -2.322788 1.4109870
## X12 -4.166581 -1.0498221 -1.832581 1.2809338
## X14 -3.357973 -1.5606477 -2.563950 1.3350011
## X16 -4.235078 -1.6607312 -2.577022 1.3609766
## X17 -4.068671 -1.3862944 -2.312635 1.0986123
## X18 -5.421759 -2.0402208 -3.057608 1.4109870
## X19 -4.200492 -1.2039728 -2.120264 1.3609766
## X20 -4.133321 -1.3862944 -1.771957 1.3862944
## X21 -6.517424 -2.1202635 -2.322788 0.9162907
## X22 -7.141616 -2.4769385 -3.036554 1.3083328
## X23 -7.141616 -1.9661129 -2.780621 0.4700036
## X24 -7.288830 -1.7147984 -2.465104 1.9021075
## X25 -7.141616 -1.7147984 -2.333044 1.8245493
## X26 -6.747507 -1.8971200 -2.577022 0.8754687
## X28 -8.932463 -1.6607312 -2.590267 1.7227666
## X29 -8.191715 -2.0402208 -2.975930 1.1631508
## X30 -6.308494 -1.3470736 -2.385967 1.4586150
## X31 -5.557525 -1.2729657 -2.040221 1.5040774
## X34 -4.458204 -1.3093333 -1.897120 1.3083328
## X35 -2.963532 -0.7133499 -1.966113 1.2809338
## X36 -2.678962 -0.4155154 -1.560648 1.9600948
## X37 -7.002855 -1.8971200 -2.703063 1.6292405
## X38 -4.458204 -1.7147984 -2.733368 1.0986123
## X39 -7.002855 -1.8325815 -3.057608 1.1314021
## X40 -4.581146 -1.5606477 -2.659260 1.4586150
## X41 -2.814127 -0.6348783 -1.427116 1.6486586
## X42 -5.779603 -1.6094379 -2.830218 1.1314021
## X43 -4.712962 -1.8971200 -2.900422 1.0647107
## X44 -2.963532 -1.8325815 -2.900422 1.4109870
## X45 -4.581146 -1.7147984 -2.322788 1.7404662
## X46 -5.941894 -2.0402208 -3.079114 0.9555114
## X47 -6.027915 -1.7147984 -2.551046 0.9932518
## X48 -7.288830 -1.8971200 -2.525729 1.1631508
## X50 -4.539232 -1.4696760 -2.120264 1.2809338
## X51 -6.210929 -1.4696760 -2.513306 1.6677068
## X53 -2.152627 -1.6094379 -2.476938 1.0296194
## X55 -4.539232 -1.6607312 -2.563950 1.2237754
## X56 -5.008267 -1.1711830 -2.513306 1.6677068
## X57 -6.117503 -2.1202635 -2.937463 1.5475625
## X59 -4.458204 -1.2039728 -1.966113 1.7227666
## X60 -5.941894 -1.7147984 -2.796881 1.5892352
## X61 -4.581146 -1.8325815 -2.864704 0.9555114
## X62 -7.141616 -1.8325815 -2.513306 1.1631508
## X63 -5.008267 -1.9661129 -2.659260 0.9932518
## X64 -3.317541 -1.0788097 -1.897120 1.3350011
## X65 -6.410533 -2.2072749 -2.830218 0.9555114
## X67 -6.308494 -1.8971200 -2.603690 1.0647107
## X68 -4.419017 -1.4696760 -2.476938 1.6486586
## X69 -8.191715 -1.8971200 -2.617296 0.9932518
## X70 -6.210929 -1.9661129 -3.015935 1.3350011
## X71 -7.445450 -1.8325815 -2.563950 0.7419373
## X72 -2.242336 -0.8641926 -1.714763 1.8718022
## X73 -4.539232 -1.7147984 -2.207275 1.0296194
## X74 -3.860022 -1.3093333 -2.120264 1.3862944
## X75 -3.130065 -1.0498221 -2.120264 1.2527630
## X76 -5.294544 -1.6094379 -2.513306 1.6094379
## X77 -4.624044 -1.2039728 -2.207275 1.3862944
## X78 -4.806353 -1.7147984 -2.733368 2.0794415
## X80 -4.306372 -1.7147984 -2.476938 1.5040774
## X81 -6.747507 -1.5606477 -2.780621 1.3862944
## X82 -6.117503 -1.6094379 -2.590267 1.7917595
## X83 -7.141616 -1.8971200 -2.645075 1.6292405
## X84 -3.725527 -2.0402208 -2.847312 1.2809338
## X85 -5.702912 -1.4696760 -2.551046 1.7047481
## X86 -7.002855 -0.9416085 -2.207275 1.6863990
## X88 -4.419017 -1.7147984 -2.645075 1.6863990
## X90 -8.417032 -2.5133061 -3.473768 1.0296194
## X93 -6.871722 -1.5141277 -2.780621 1.5475625
## X94 -5.008267 -0.6931472 -1.832581 2.1633230
## X95 -6.027915 -1.4696760 -1.966113 1.3350011
## X96 -5.859197 -2.0402208 -2.748872 1.4586150
## X97 -7.791589 -1.7147984 -2.830218 1.7917595
## X98 -7.288830 -1.8325815 -2.590267 1.0986123
## X99 -3.378594 -1.6607312 -2.590267 1.6094379
## X100 -5.117801 -1.7147984 -2.975930 1.5686159
## X103 -5.062260 -1.5606477 -2.453408 1.5686159
## X104 -4.458204 -1.5606477 -2.830218 1.4350845
## X105 -6.517424 -1.4271164 -2.631089 1.3609766
## X107 -3.442152 -1.6607312 -2.501036 1.5686159
## X108 -5.859197 -1.2378744 -2.207275 2.1517622
## X109 -7.983999 -1.8971200 -2.995732 1.1314021
## X110 -9.936906 -3.3242363 -3.688879 0.8754687
## X111 -5.941894 -1.3862944 -2.659260 1.4816045
## X112 -4.854854 -0.8915981 -1.609438 1.8405496
## X113 -2.152627 -1.1394343 -1.897120 1.4816045
## X114 -5.008267 -1.8325815 -2.302585 1.5686159
## X115 -3.751559 -0.8439701 -2.120264 1.8082888
## X117 -6.517424 -1.7147984 -2.207275 1.3862944
## X118 -5.233853 -1.7719568 -2.590267 1.7404662
## X121 -3.976069 -1.3862944 -2.207275 2.0794415
## X123 -7.983999 -1.8325815 -2.703063 1.1939225
## X124 -6.308494 -1.7719568 -2.525729 1.8718022
## X126 -4.904631 -1.8325815 -3.079114 0.8329091
## X128 -2.442874 -0.8209806 -1.237874 1.7917595
## X129 -5.859197 -1.7147984 -3.324236 1.3609766
## X130 -5.702912 -1.3862944 -2.120264 1.2237754
## X131 -5.628941 -1.5141277 -2.538307 0.9162907
## X132 -4.539232 -1.7719568 -2.577022 1.3862944
## X133 -2.555800 -0.8209806 -1.660731 2.1162555
## X134 -4.624044 -1.9661129 -2.120264 1.7227666
## X135 -5.557525 -1.3862944 -2.207275 1.8082888
## X136 -2.678962 -1.2039728 -2.407946 1.5260563
## X137 -7.002855 -2.1202635 -2.733368 0.7419373
## X139 -5.702912 -1.5141277 -2.441847 1.2527630
## X140 -5.233853 -1.8325815 -2.645075 1.8718022
## X141 -4.539232 -1.5606477 -2.302585 1.6486586
## X143 -5.117801 -1.3470736 -2.396896 1.1939225
## X144 -5.233853 -1.7719568 -2.748872 1.5260563
## X145 -8.191715 -2.1202635 -2.847312 1.0647107
## X146 -4.166581 -1.5606477 -2.302585 2.0412203
## X147 -5.062260 -1.8325815 -2.733368 1.8082888
## X148 -3.888287 -0.9416085 -2.302585 2.0281482
## X149 -6.629589 -1.5606477 -2.780621 1.1314021
## X152 -3.399492 -1.0788097 -2.040221 2.1972246
## X153 -4.380670 -1.2378744 -2.465104 2.0918641
## X154 -4.806353 -1.6607312 -2.513306 0.9555114
## X155 -4.955747 -1.4271164 -2.120264 1.5892352
## X156 -5.233853 -1.2039728 -2.120264 1.3862944
## X157 -5.357143 -1.6094379 -2.577022 1.4586150
## X158 -5.859197 -1.7719568 -2.937463 1.1939225
## X159 -6.027915 -1.6607312 -2.513306 0.6931472
## X160 -5.294544 -1.2729657 -2.375156 1.3350011
## X161 -4.712962 -1.6094379 -2.577022 1.1939225
## X162 -5.357143 -1.8971200 -3.057608 1.0986123
## X163 -6.410533 -2.0402208 -3.411248 0.9555114
## X165 -6.210929 -2.0402208 -3.506558 1.1314021
## X166 -6.308494 -1.8971200 -2.718101 1.1631508
## X167 -6.210929 -1.6094379 -2.882404 1.5475625
## X168 -5.294544 -1.0788097 -2.120264 1.2809338
## X169 -8.417032 -1.8971200 -2.645075 1.5475625
## X170 -5.357143 -1.2729657 -2.353878 1.2809338
## X171 -4.624044 -1.6607312 -2.703063 1.6486586
## X172 -6.210929 -1.4271164 -2.302585 1.3609766
## X174 -4.166581 -0.6348783 -1.386294 1.9600948
## X175 -2.963532 -0.5978370 -1.609438 2.2721259
## X176 -3.649844 -1.7147984 -2.718101 1.3609766
## X177 -6.871722 -1.6094379 -2.937463 1.5475625
## X178 -6.871722 -1.8325815 -2.577022 0.7884574
## X179 -3.531121 -0.8400523 -1.469676 1.8562980
## X180 -6.629589 -1.5141277 -2.501036 1.9878743
## X181 -6.410533 -2.0402208 -3.352407 1.1631508
## X182 -6.117503 -1.3862944 -2.207275 1.4109870
## X183 -4.166581 -1.0216512 -1.832581 2.0014800
## X184 -5.557525 -1.7719568 -2.673649 1.6863990
## X185 -3.699905 -1.4696760 -2.120264 1.4816045
## X186 -6.747507 -1.9661129 -2.796881 1.3083328
## X189 -5.233853 -2.1202635 -2.796881 1.3862944
## X190 -7.141616 -1.7719568 -2.995732 1.7917595
## X191 -5.941894 -1.6607312 -2.617296 1.7404662
## X192 -5.779603 -1.7719568 -2.847312 1.4109870
## X193 -5.294544 -1.6607312 -2.385967 1.4350845
## X194 -4.581146 -1.6094379 -2.488915 1.5475625
## X195 -7.445450 -1.4271164 -2.703063 1.0986123
## X197 -5.062260 -1.3862944 -2.302585 1.7917595
## X198 -8.191715 -1.6607312 -3.146555 1.3083328
## X200 -5.702912 -1.6094379 -2.419119 1.5892352
## X201 -4.904631 -1.7147984 -2.733368 1.4586150
## X202 -5.859197 -2.3434071 -2.617296 0.8754687
## X205 -6.117503 -1.7147984 -2.937463 1.2527630
## X208 -5.859197 -1.5141277 -2.577022 1.7047481
## X210 -4.380670 -1.4696760 -1.966113 1.7404662
## X212 -6.517424 -1.8325815 -2.563950 1.0647107
## X213 -5.859197 -1.5606477 -2.364460 1.6486586
## X214 -2.338769 -1.7719568 -2.040221 1.1631508
## X215 -7.612597 -2.2072749 -2.937463 0.7419373
## X216 -3.976069 -1.4271164 -2.673649 1.3083328
## X218 -4.759074 -1.1711830 -2.120264 1.7917595
## X219 -6.871722 -1.3862944 -2.488915 1.0986123
## X220 -6.871722 -2.0402208 -2.995732 1.1939225
## X223 -7.141616 -1.1711830 -1.966113 1.8718022
## X224 -6.117503 -1.5606477 -2.396896 1.2809338
## X225 -8.417032 -1.7719568 -2.703063 1.3083328
## X226 -6.210929 -1.8325815 -3.123566 1.8870696
## X227 -6.210929 -0.8915981 -1.771957 2.0412203
## X228 -8.417032 -2.0402208 -3.194183 0.7884574
## X229 -7.002855 -2.2072749 -2.937463 1.6486586
## X230 -4.270363 -1.6094379 -2.207275 1.5260563
## X231 -5.779603 -1.5141277 -2.563950 1.2527630
## X232 -6.747507 -1.7719568 -3.079114 1.3083328
## X233 -5.859197 -1.2378744 -2.501036 1.5686159
## X234 -4.624044 -1.1711830 -2.040221 1.8405496
## X236 -7.445450 -2.1202635 -3.170086 1.1631508
## X237 -5.488510 -1.0788097 -2.385967 1.7404662
## X239 -7.612597 -2.5902672 -3.057608 1.1631508
## X240 -6.517424 -2.2072749 -2.577022 0.9932518
## X241 -4.006378 -1.3862944 -2.040221 1.3350011
## X242 -4.581146 -0.2744368 -1.469676 1.5040774
## X243 -5.941894 -1.7719568 -2.419119 1.4350845
## X244 -4.904631 -1.5141277 -2.396896 1.3862944
## X245 -8.191715 -2.6736488 -3.540459 0.8329091
## X246 -6.517424 -1.1394343 -2.120264 1.8562980
## X247 -7.141616 -2.3968958 -3.473768 0.6418539
## X249 -7.288830 -1.9661129 -3.411248 1.1631508
## X250 -5.557525 -1.9661129 -2.748872 1.1939225
## X251 -6.027915 -2.0402208 -2.918771 1.2527630
## X253 -4.200492 -1.5606477 -2.207275 1.6486586
## X254 -6.747507 -1.3862944 -2.207275 1.8870696
## X255 -3.601291 -0.9162907 -2.040221 1.8082888
## X256 -8.417032 -2.2072749 -3.079114 0.8754687
## X257 -6.210929 -1.8971200 -2.631089 1.0647107
## X258 -4.100692 -1.2729657 -2.322788 0.8754687
## X260 -3.508403 -1.3862944 -2.577022 1.4816045
## X261 -6.629589 -1.7147984 -2.631089 0.9932518
## X262 -5.233853 -1.5141277 -2.918771 1.2527630
## X263 -4.806353 -1.1711830 -2.040221 1.6863990
## X264 -8.417032 -1.3470736 -1.897120 2.0014800
## X265 -6.210929 -1.7719568 -2.780621 1.4109870
## X267 -5.859197 -1.8971200 -2.525729 1.8718022
## X268 -4.806353 -1.4696760 -2.353878 1.4109870
## X269 -5.702912 -2.2072749 -3.101093 1.1314021
## X270 -6.308494 -1.8325815 -3.244194 1.5686159
## X271 -4.806353 -1.5606477 -2.465104 1.6094379
## X272 -7.288830 -1.2378744 -2.441847 1.5686159
## X273 -7.002855 -1.9661129 -3.036554 1.4109870
## X274 -5.233853 -1.6094379 -2.864704 1.3083328
## X275 -6.117503 -1.4696760 -2.513306 1.6094379
## X277 -3.130065 -1.3093333 -2.207275 1.4586150
## X278 -4.955747 -2.0402208 -2.430418 1.5475625
## X279 -6.871722 -1.7147984 -2.302585 1.5686159
## X281 -4.806353 -1.7719568 -2.551046 1.5892352
## X282 -4.498264 -1.4696760 -2.040221 1.2527630
## X283 -4.343131 -1.1394343 -2.207275 1.6094379
## X287 -5.174970 -1.1711830 -2.120264 1.1314021
## X289 -6.308494 -1.7147984 -2.764621 1.4586150
## X290 -7.002855 -1.8971200 -2.688248 0.9555114
## X291 -6.517424 -1.6607312 -2.631089 1.6863990
## X292 -7.141616 -1.5141277 -2.525729 1.8405496
## X294 -9.936906 -1.6607312 -2.207275 1.5040774
## X297 -7.288830 -1.9661129 -2.864704 1.4350845
## X298 -6.410533 -1.6607312 -2.590267 1.2809338
## X299 -5.941894 -1.7147984 -2.364460 1.7047481
## X301 -6.871722 -1.9661129 -2.864704 1.2527630
## X302 -6.410533 -1.7147984 -2.603690 1.3609766
## X303 -6.629589 -1.5141277 -2.918771 1.5686159
## X304 -7.288830 -1.7719568 -2.617296 1.3862944
## X305 -4.498264 -1.3093333 -2.538307 1.7227666
## X306 -6.210929 -1.0216512 -1.609438 1.8870696
## X307 -4.581146 -1.2039728 -1.560648 2.2512918
## X308 -4.581146 -1.5141277 -1.832581 1.7917595
## X311 -3.420676 -0.8915981 -2.040221 1.5686159
## X312 -7.002855 -1.5141277 -2.040221 1.3609766
## X313 -7.288830 -1.3093333 -2.302585 1.6486586
## X314 -7.983999 -2.0402208 -3.352407 1.3083328
## X315 -3.357973 -0.9162907 -1.560648 1.5892352
## X316 -4.854854 -1.2378744 -2.476938 1.1939225
## X317 -6.747507 -1.6607312 -2.617296 1.2527630
## X320 -3.508403 -0.3856625 -1.386294 1.4350845
## X321 -3.130065 -0.8915981 -2.207275 1.3350011
## X322 -7.445450 -1.0216512 -2.120264 1.4109870
## X323 -5.779603 -1.8325815 -2.764621 1.3083328
## X324 -6.517424 -2.6592600 -3.101093 1.2237754
## X325 -6.308494 -1.5606477 -2.847312 1.6677068
## X326 -6.210929 -2.1202635 -2.918771 1.3083328
## X327 -4.498264 -1.6094379 -2.120264 1.9740810
## X329 -6.629589 -1.6607312 -2.796881 1.1939225
## X330 -4.759074 -0.9416085 -1.514128 1.9169226
## X331 -5.421759 -1.4696760 -2.733368 1.2809338
## X332 -5.008267 -1.6094379 -2.617296 1.6863990
## X333 -4.806353 -1.4271164 -2.385967 1.5475625
## Apolipoprotein_E Apolipoprotein_H B_Lymphocyte_Chemoattractant_BL
## X1 3.7545215 -0.15734908 2.2969819
## X2 3.0971187 -0.57539617 1.6731213
## X3 2.7530556 -0.34483937 1.6731213
## X5 3.0671471 0.66263455 2.2969819
## X6 0.5911464 0.09715030 2.4798381
## X7 4.2548002 -0.34483937 1.6731213
## X8 1.9385358 0.09715030 3.7036702
## X9 2.7200688 -1.26939244 2.3713615
## X11 2.7200688 -0.28367882 1.8527528
## X12 3.1563503 -0.78459824 2.6867663
## X14 2.4440754 -0.21347474 1.6731213
## X16 2.5848812 -0.53172814 2.9757467
## X17 2.7857346 -0.03027441 3.0064666
## X18 2.0627326 -0.48946700 1.2740115
## X19 1.5787922 -0.23651381 2.2786154
## X20 2.3713615 0.09715030 2.0627326
## X21 2.4079204 -0.37004744 1.4308338
## X22 2.8181133 -0.80232932 1.9805094
## X23 1.8527528 -0.63604036 0.7317775
## X24 3.7797161 -0.54612169 1.8527528
## X25 1.8088944 -0.38282097 1.8527528
## X26 2.6531400 -0.35738780 1.8527528
## X28 3.6261997 -0.24816638 1.8527528
## X29 1.8959582 -0.63604036 2.3713615
## X30 1.8527528 -0.54612169 0.7987698
## X31 2.7200688 0.00000000 2.0219013
## X34 1.8959582 0.00000000 2.3713615
## X35 2.1427912 -0.44849801 1.9805094
## X36 3.0971187 0.59114642 3.4937139
## X37 2.5848812 -0.46201723 2.1820549
## X38 2.5848812 -0.56067607 2.3713615
## X39 3.1856203 -0.59028711 2.0627326
## X40 2.2591348 -0.38282097 2.0627326
## X41 2.7200688 0.44019756 2.6867663
## X42 2.5152196 -0.38282097 2.5152196
## X43 1.9805094 -0.63604036 2.1427912
## X44 2.8501989 -0.37004744 1.6731213
## X45 3.7797161 0.18913439 2.1820549
## X46 3.2146659 -0.73298708 2.6531400
## X47 2.0627326 -0.46201723 2.3713615
## X48 4.2548002 -0.57539617 2.5848812
## X50 3.6000471 -0.10317121 2.3713615
## X51 3.3566831 -0.38282097 1.9805094
## X53 3.0369315 -0.48946700 1.2740115
## X55 2.8181133 -0.38282097 0.7317775
## X56 3.6261997 -0.03027441 2.6867663
## X57 3.6521859 -0.80232932 2.6867663
## X59 2.5502306 -0.03027441 2.9757467
## X60 2.6531400 -0.37004744 1.6731213
## X61 2.7530556 -0.33239959 1.2740115
## X62 2.4440754 -1.11122739 1.2740115
## X63 2.9447661 -0.34483937 2.3713615
## X64 1.5303762 0.18913439 2.9757467
## X65 3.0671471 -0.83865049 1.6731213
## X67 2.9447661 -0.80232932 1.9805094
## X68 3.0064666 -0.17955518 2.3713615
## X69 1.8527528 -0.74993753 1.2740115
## X70 3.3566831 -0.65167154 2.1820549
## X71 3.1563503 -0.76713789 2.0627326
## X72 4.0237466 0.27662577 2.6531400
## X73 4.0237466 -0.34483937 2.0627326
## X74 1.6731213 0.00000000 2.0219013
## X75 2.6191813 0.00000000 2.3713615
## X76 3.3005016 -0.20208470 2.6867663
## X77 2.6531400 0.00000000 1.9805094
## X78 3.6521859 0.27662577 1.8527528
## X80 2.4079204 -0.15734908 2.0219013
## X81 3.3566831 -0.35738780 1.9805094
## X82 2.2591348 -0.47567232 2.7530556
## X83 2.8181133 -0.74993753 2.3713615
## X84 2.2591348 -0.56067607 1.9805094
## X85 4.2548002 -0.08200644 1.9805094
## X86 4.0237466 0.59114642 3.4937139
## X88 2.7857346 -0.04049051 2.4440754
## X90 1.5787922 -0.99753895 1.5303762
## X93 3.0671471 0.09715030 2.6867663
## X94 4.6844119 0.00000000 2.9757467
## X95 2.5502306 -0.38282097 1.2740115
## X96 3.0971187 -0.51749076 2.1820549
## X97 3.4937139 -0.54612169 2.1820549
## X98 2.9447661 -0.87620360 0.7987698
## X99 3.0064666 -0.39571116 2.6531400
## X100 2.8181133 -0.50340513 1.9805094
## X103 4.2548002 -0.53172814 2.3713615
## X104 2.3713615 -0.43511112 1.9805094
## X105 4.0237466 -0.46201723 1.2740115
## X107 2.1427912 -0.15734908 1.8527528
## X108 3.1563503 0.27662577 2.0219013
## X109 3.4120676 -0.76713789 1.2740115
## X110 1.1637797 -1.13570251 1.4810717
## X111 2.3713615 -0.40872089 2.3713615
## X112 3.2146659 -0.17955518 2.2969819
## X113 3.2146659 0.66263455 2.1820549
## X114 1.3796139 -0.32006598 2.1820549
## X115 2.1427912 -0.03027441 2.9757467
## X117 3.1268514 -0.69979867 2.2969819
## X118 4.0237466 -0.37004744 1.8527528
## X121 2.9135187 -0.03027441 2.6867663
## X123 1.9385358 -0.87620360 1.6731213
## X124 2.4798381 0.36016589 2.3713615
## X126 1.9385358 -0.44849801 2.3713615
## X128 2.8819985 0.18913439 2.3713615
## X129 1.7643559 -0.53172814 2.6867663
## X130 3.5205617 0.09715030 3.0064666
## X131 2.6867663 -0.51749076 2.2208309
## X132 2.4079204 0.00000000 1.6731213
## X133 3.2146659 0.51708817 2.1820549
## X134 2.3343863 0.09715030 2.7530556
## X135 4.0237466 -0.32006598 1.6731213
## X136 2.1427912 -0.69979867 2.3713615
## X137 1.8088944 -0.73298708 0.7317775
## X139 1.9805094 -0.40872089 2.1820549
## X140 3.4666845 -0.19077873 1.8527528
## X141 3.0671471 -0.08200644 2.1820549
## X143 2.6867663 -0.12462010 1.8527528
## X144 2.3343863 -0.44849801 1.8527528
## X145 2.4440754 -0.48946700 0.7987698
## X146 3.0971187 0.18913439 2.4798381
## X147 3.0971187 -0.89547834 1.8527528
## X148 3.0671471 0.66263455 3.4937139
## X149 3.4394702 -0.63604036 1.2740115
## X152 4.8854423 -0.27174693 2.4798381
## X153 3.3566831 0.18913439 2.1820549
## X154 2.9135187 -0.66750355 1.2740115
## X155 1.6263611 -0.28367882 1.6731213
## X156 3.1856203 0.09715030 1.6731213
## X157 3.0064666 -0.39571116 2.0219013
## X158 3.0671471 -0.69979867 1.4810717
## X159 2.3343863 -0.65167154 1.8813120
## X160 4.2548002 -0.42185317 2.0627326
## X161 3.3566831 -0.07152751 2.3713615
## X162 3.2434918 -0.65167154 1.9805094
## X163 2.4079204 -0.76713789 1.9805094
## X165 2.6867663 -0.54612169 1.4308338
## X166 2.7200688 -0.66750355 0.9269604
## X167 4.0237466 -0.60535429 2.5152196
## X168 3.7797161 -0.39571116 2.0627326
## X169 2.5502306 -0.39571116 1.8527528
## X170 2.3343863 -0.25991011 1.4308338
## X171 3.6780085 -0.17955518 1.9805094
## X172 2.5502306 -0.43511112 1.6731213
## X174 2.7857346 -0.35738780 2.3713615
## X175 2.8501989 0.09715030 2.0219013
## X176 2.6191813 -0.71627709 2.6867663
## X177 2.1030230 -0.51749076 1.5303762
## X178 1.8088944 -0.63604036 1.9805094
## X179 4.0237466 0.44019756 2.4798381
## X180 2.9135187 -0.25991011 3.0064666
## X181 2.1030230 -0.51749076 1.7643559
## X182 3.3844731 -0.40872089 1.6731213
## X183 1.1637797 0.09715030 1.8527528
## X184 2.9447661 -0.35738780 2.1820549
## X185 2.8819985 -0.66750355 2.3713615
## X186 2.1820549 -0.40872089 1.4308338
## X189 2.8819985 -0.66750355 1.4308338
## X190 3.5737252 -0.38282097 1.9805094
## X191 2.5502306 -0.43511112 1.9805094
## X192 2.9447661 -0.76713789 1.9805094
## X193 2.9447661 -0.27174693 2.3713615
## X194 1.5787922 -0.22495053 1.9805094
## X195 3.7545215 -0.56067607 1.2740115
## X197 2.7857346 -0.01003016 2.1820549
## X198 2.3343863 -0.56067607 0.9269604
## X200 3.0369315 -0.42185317 2.0219013
## X201 2.7200688 -0.32006598 2.3713615
## X202 2.1820549 -0.66750355 0.7987698
## X205 3.1563503 -0.40872089 1.8527528
## X208 2.9757467 0.09715030 1.9805094
## X210 3.0369315 -0.16841247 1.8527528
## X212 1.8959582 -0.15734908 1.9805094
## X213 2.8819985 0.18913439 1.4308338
## X214 2.2969819 -0.23651381 1.2740115
## X215 2.6531400 -0.60535429 1.2740115
## X216 1.3796139 0.09715030 2.3713615
## X218 2.3713615 0.44019756 2.9757467
## X219 2.6191813 -0.38282097 1.9805094
## X220 3.1856203 -0.65167154 2.3713615
## X223 2.4079204 -0.03027441 1.6731213
## X224 2.9447661 -0.24816638 1.2740115
## X225 3.6780085 -0.66750355 1.2740115
## X226 3.3844731 -0.56067607 2.6867663
## X227 2.7200688 0.92696036 2.2969819
## X228 1.8959582 -0.68354345 0.7987698
## X229 1.1067498 -1.06412706 1.4308338
## X230 4.0237466 0.00000000 1.8527528
## X231 3.0064666 -0.83865049 1.8527528
## X232 3.6521859 -0.16841247 2.6867663
## X233 1.0483341 -0.23651381 1.9805094
## X234 3.7036702 0.09715030 1.4308338
## X236 3.1563503 -0.60535429 1.4308338
## X237 2.8819985 0.09715030 2.6867663
## X239 0.9884391 -0.76713789 0.7987698
## X240 2.2208309 -0.39571116 0.7987698
## X241 3.3844731 -0.21347474 1.8527528
## X242 1.2195081 0.24675221 2.2208309
## X243 2.3343863 -0.07152751 2.6867663
## X244 2.7200688 -0.32006598 2.3713615
## X245 1.1637797 -0.83865049 1.5303762
## X246 3.4666845 0.36016589 1.6731213
## X247 2.0627326 -0.69979867 1.5303762
## X249 3.6000471 -0.39571116 2.1820549
## X250 2.7200688 -2.23379225 2.0627326
## X251 2.2969819 -0.71627709 2.1820549
## X253 2.1820549 -0.82034264 2.8501989
## X254 4.2548002 -0.42185317 2.2969819
## X255 3.3844731 -0.21347474 2.3713615
## X256 2.7857346 -0.74993753 1.9805094
## X257 1.8088944 -0.62060338 1.5303762
## X258 3.0971187 -0.16841247 2.3713615
## X260 2.9757467 0.09715030 2.3713615
## X261 2.7200688 -0.66750355 1.5303762
## X262 2.5848812 -0.57539617 1.9805094
## X263 3.4394702 -0.24816638 2.3713615
## X264 2.0219013 0.27662577 2.3713615
## X265 3.5472311 -0.47567232 2.3713615
## X267 3.6261997 -0.48946700 2.1820549
## X268 3.3566831 -0.08200644 1.9805094
## X269 2.5152196 -0.71627709 0.7987698
## X270 3.2434918 -0.93510686 1.5303762
## X271 2.5502306 0.18913439 1.9805094
## X272 3.6000471 -0.05077067 2.3713615
## X273 3.2721023 -0.30783617 2.6867663
## X274 2.7200688 -0.51749076 1.5303762
## X275 3.6780085 -0.13545431 1.6731213
## X277 2.9447661 -0.51749076 1.4308338
## X278 3.0671471 -0.14636351 1.2740115
## X279 2.5848812 -0.32006598 0.9884391
## X281 2.8181133 -0.30783617 1.8527528
## X282 2.2591348 -0.06111597 2.3713615
## X283 4.8854423 -0.03027441 4.0237466
## X287 3.6000471 -0.46201723 1.2740115
## X289 1.8088944 -0.35738780 1.2740115
## X290 1.8088944 -0.50340513 1.0483341
## X291 3.1856203 -0.82034264 1.8527528
## X292 3.7797161 -0.05077067 2.2969819
## X294 2.9135187 -0.37004744 1.6731213
## X297 3.0971187 -0.53172814 2.1820549
## X298 1.6731213 -0.16841247 1.6731213
## X299 3.2434918 -0.60535429 1.8527528
## X301 1.9385358 -0.30783617 2.3713615
## X302 1.3796139 -0.32006598 0.7987698
## X303 2.8501989 -0.16841247 1.9805094
## X304 2.4798381 -0.63604036 1.9805094
## X305 4.6844119 -0.80232932 1.9805094
## X306 3.1856203 -0.12462010 2.2969819
## X307 3.3005016 0.36016589 2.0219013
## X308 2.2208309 0.18913439 1.2740115
## X311 1.5787922 -0.19077873 2.6867663
## X312 4.0237466 -0.66750355 2.3713615
## X313 2.3343863 -0.28367882 2.6867663
## X314 2.5502306 -0.85726607 1.5303762
## X315 3.3286939 0.27662577 1.6731213
## X316 2.8819985 -0.65167154 1.6731213
## X317 3.4937139 -0.63604036 1.2740115
## X320 2.2208309 0.59114642 2.0627326
## X321 1.1637797 -0.01003016 1.2740115
## X322 5.4441788 0.36016589 2.6867663
## X323 3.1268514 -0.03027441 2.3713615
## X324 2.2208309 -0.83865049 1.6731213
## X325 3.7036702 0.00000000 2.9757467
## X326 2.3343863 -0.60535429 2.5152196
## X327 3.1268514 -0.06111597 2.7530556
## X329 3.4666845 -0.87620360 1.9805094
## X330 1.4308338 0.86378110 1.8527528
## X331 1.8088944 -0.38282097 2.3713615
## X332 2.2208309 -0.19077873 2.6867663
## X333 3.6000471 -0.25991011 2.6867663
## BMP_6 Beta_2_Microglobulin Betacellulin C_Reactive_Protein CD40
## X1 -2.2007445 0.69314718 34 -4.074542 -0.7964147
## X2 -1.7280531 0.47000363 53 -6.645391 -1.2733760
## X3 -2.0624206 0.33647224 49 -8.047190 -1.2415199
## X5 -1.2415199 0.33647224 67 -4.342806 -0.9240345
## X6 -1.8774117 -0.54472718 51 -7.561682 -1.7844998
## X7 -1.8452133 -0.04082199 41 -7.581100 -1.0965412
## X8 -1.9829118 -0.07257069 42 -6.165818 -1.8643733
## X9 -1.6752524 0.00000000 58 -7.070274 -1.4919984
## X11 -1.4130880 0.18232156 32 -6.645391 -1.3405665
## X12 -1.9679750 -0.10536052 43 -4.828314 -1.3405665
## X14 -2.0624206 0.40546511 53 -5.083206 -1.0965412
## X16 -1.9679750 0.33647224 58 -5.051457 -1.3761017
## X17 -2.0458231 0.09531018 51 -5.278515 -1.4130880
## X18 -1.6752524 -0.02020271 67 -5.626821 -1.5342758
## X19 -1.6752524 0.18232156 42 -6.980326 -1.4130880
## X20 -2.0624206 0.18232156 46 -5.221356 -1.3405665
## X21 -1.7280531 0.26236426 51 -6.571283 -1.1808680
## X22 -2.2007445 0.74193734 52 -5.051457 -0.9240345
## X23 -1.8906716 -0.12783337 46 -5.546779 -1.4130880
## X24 -2.0458231 0.33647224 42 -6.377127 -1.0441270
## X25 -1.8774117 -0.13926207 51 -6.812445 -1.4516659
## X26 -2.0458231 0.09531018 59 -8.468403 -1.1808680
## X28 -2.0458231 0.33647224 32 -6.645391 -1.3405665
## X29 -1.6752524 -0.08338161 61 -6.119298 -1.6256074
## X30 -1.8452133 -0.22314355 41 -6.165818 -1.2415199
## X31 -2.2319708 0.26236426 60 -4.074542 -0.9016377
## X34 -2.1516047 -0.01005034 51 -5.449140 -1.3405665
## X35 -1.4130880 -0.37106368 71 -5.599422 -1.3405665
## X36 -2.1516047 0.18232156 51 -3.963316 -1.2107086
## X37 -2.0458231 0.33647224 59 -7.849364 -1.2733760
## X38 -2.7611525 0.00000000 65 -4.815891 -1.2415199
## X39 -2.0624206 0.26236426 37 -7.402052 -1.2733760
## X40 -1.7280531 0.47000363 46 -6.032287 -1.3405665
## X41 -1.9679750 0.09531018 43 -5.744604 -1.4130880
## X42 -1.9679750 0.18232156 51 -6.437752 -1.2733760
## X43 -2.2319708 0.09531018 67 -5.298317 -1.3063602
## X44 -2.2873761 0.58778666 46 -4.879607 -1.2107086
## X45 -2.0458231 0.53062825 51 -7.600902 -0.9943519
## X46 -2.2873761 0.40546511 46 -5.381699 -1.1808680
## X47 -2.3867382 -0.30110509 51 -6.571283 -1.4130880
## X48 -2.2319708 0.58778666 60 -4.605170 -0.9016377
## X50 -0.8166252 0.26236426 51 -4.803621 -1.2415199
## X51 -1.8452133 0.33647224 42 -4.853632 -0.9240345
## X53 -1.7280531 0.33647224 46 -6.571283 -1.3405665
## X55 -2.1611633 0.53062825 53 -7.684284 -1.2415199
## X56 -1.9679750 0.09531018 51 -5.521461 -1.3761017
## X57 -2.2007445 0.58778666 42 -5.952244 -1.3761017
## X59 -1.6752524 0.33647224 43 -6.812445 -1.3063602
## X60 -1.9981504 0.00000000 60 -5.203007 -1.3761017
## X61 -1.5787229 -0.08338161 26 -7.182192 -1.0441270
## X62 -1.9041616 -0.26136476 51 -6.032287 -1.2107086
## X63 -2.2535986 0.26236426 51 -5.952244 -1.2733760
## X64 -1.8452133 -0.21072103 51 -3.816713 -1.3063602
## X65 -1.8452133 0.09531018 60 -7.035589 -1.1519318
## X67 -1.9679750 0.09531018 51 -7.957577 -1.3063602
## X68 -1.8452133 0.18232156 61 -6.377127 -1.3405665
## X69 -1.3761017 -0.47803580 67 -5.654992 -1.5342758
## X70 -2.0458231 0.09531018 42 -6.319969 -1.3063602
## X71 -1.7280531 0.09531018 49 -3.575551 -1.2733760
## X72 -2.6486592 0.99325177 28 -2.937463 -0.9016377
## X73 -2.0624206 0.40546511 46 -5.099467 -1.1238408
## X74 -1.5787229 -0.46203546 51 -5.167289 -1.3761017
## X75 -2.1611633 0.18232156 46 -6.265901 -1.2733760
## X76 -2.0540751 0.26236426 51 -7.169120 -1.1808680
## X77 -1.5787229 0.26236426 68 -4.135167 -1.1519318
## X78 -2.0458231 0.53062825 51 -6.119298 -0.9943519
## X80 -1.8452133 0.00000000 41 -3.194183 -1.2107086
## X81 -2.1516047 0.26236426 55 -5.449140 -1.2415199
## X82 -2.0458231 0.18232156 32 -5.878136 -1.3405665
## X83 -1.8452133 0.09531018 52 -6.377127 -1.3761017
## X84 -1.8452133 -0.18632958 51 -5.318520 -1.2415199
## X85 -2.3867382 0.47000363 43 -6.437752 -1.0699846
## X86 -1.4919984 0.58778666 51 -4.422849 -0.9240345
## X88 -2.2319708 0.33647224 74 -5.654992 -1.2107086
## X90 -1.9829118 -0.31471074 72 -4.509860 -1.8452133
## X93 -1.9829118 0.47000363 52 -5.184989 -1.5342758
## X94 -2.1516047 0.83290912 43 -5.776353 -0.7007809
## X95 -1.6752524 -0.09431068 51 -6.437752 -0.9943519
## X96 -1.8774117 0.26236426 66 -5.115996 -1.2415199
## X97 -1.6256074 0.18232156 42 -8.468403 -1.1808680
## X98 -2.2319708 -0.30110509 71 -4.744432 -1.3761017
## X99 -2.2873761 0.33647224 37 -4.199705 -0.9943519
## X100 -2.1516047 0.33647224 51 -5.744604 -1.1808680
## X103 -2.6486592 0.64185389 42 -6.074846 -0.6826401
## X104 -2.5370840 0.40546511 47 -5.809143 -1.2733760
## X105 -2.2007445 0.33647224 46 -6.948577 -1.0965412
## X107 -2.2007445 0.26236426 51 -6.502290 -0.9240345
## X108 -1.9981504 -0.04082199 46 -6.214608 -0.9240345
## X109 -1.7280531 0.09531018 42 -6.502290 -1.1238408
## X110 -1.8452133 -0.26136476 74 -7.799353 -1.3405665
## X111 -1.9829118 0.09531018 61 -6.502290 -1.2733760
## X112 -2.6694708 0.26236426 67 -5.115996 -1.0189283
## X113 -1.7280531 0.47000363 32 -5.472671 -1.3761017
## X114 -1.4919984 -0.54472718 59 -5.914504 -1.5787229
## X115 -2.3867382 0.18232156 58 -5.991465 -1.3405665
## X117 -1.6256074 0.33647224 60 -3.963316 -1.1808680
## X118 -2.0458231 0.74193734 51 -5.744604 -1.0699846
## X121 -2.3867382 0.47000363 58 -5.278515 -0.9240345
## X123 -1.4516659 -0.49429632 60 -4.779524 -1.3405665
## X124 -1.8452133 0.18232156 61 -5.035953 -1.2415199
## X126 -1.5787229 0.09531018 58 -5.203007 -1.4516659
## X128 -1.9679750 0.33647224 58 -4.710531 -1.1519318
## X129 -1.9679750 -0.04082199 51 -7.849364 -1.6752524
## X130 -1.7280531 0.33647224 59 -4.947660 -1.4516659
## X131 -2.2873761 0.09531018 53 -5.744604 -1.2733760
## X132 -1.6752524 0.00000000 64 -7.581100 -1.3063602
## X133 -1.7280531 0.09531018 20 -8.078938 -1.2107086
## X134 -1.8774117 0.33647224 51 -4.892852 -1.0965412
## X135 -2.2319708 0.09531018 60 -4.074542 -1.0441270
## X136 -2.5370840 0.18232156 61 -6.074846 -1.3063602
## X137 -1.6256074 -0.34249031 46 -3.729701 -1.4516659
## X139 -1.8774117 -0.12783337 51 -7.523941 -1.5787229
## X140 -2.0458231 0.09531018 42 -6.319969 -1.2415199
## X141 -1.6752524 0.09531018 51 -5.683980 -1.2415199
## X143 -1.6752524 0.09531018 41 -6.265901 -1.3761017
## X144 -1.8774117 0.09531018 59 -6.377127 -1.3063602
## X145 -2.2319708 -0.04082199 67 -7.581100 -1.3761017
## X146 -2.2007445 0.33647224 42 -7.070274 -1.0699846
## X147 -2.2646732 0.58778666 42 -4.779524 -1.0965412
## X148 -2.2007445 0.18232156 10 -3.123566 -1.3063602
## X149 -1.8906716 -0.01005034 37 -4.342806 -1.4130880
## X152 -1.8774117 0.74193734 32 -5.878136 -1.0965412
## X153 -2.2646732 0.40546511 32 -4.017384 -1.3761017
## X154 -2.6486592 0.09531018 37 -5.278515 -1.2107086
## X155 -1.5787229 -0.26136476 55 -5.020686 -1.2733760
## X156 -1.6256074 0.18232156 46 -4.733004 -1.2415199
## X157 -1.8452133 0.33647224 55 -6.319969 -0.9240345
## X158 -2.1611633 0.09531018 53 -5.132803 -0.9943519
## X159 -1.8452133 -0.02020271 58 -5.924465 -1.4516659
## X160 -1.5342758 0.47000363 37 -4.892852 -0.9469346
## X161 -1.9679750 0.40546511 58 -3.611918 -1.1808680
## X162 -1.5787229 0.47000363 52 -6.437752 -1.1808680
## X163 -2.2007445 -0.05129329 61 -7.250246 -1.4516659
## X165 -1.4919984 0.18232156 32 -7.293418 -1.3761017
## X166 -1.9679750 -0.16251893 58 -8.334872 -1.2415199
## X167 -1.6752524 0.58778666 68 -6.812445 -0.9016377
## X168 -2.0624206 0.09531018 46 -5.914504 -1.3761017
## X169 -2.0458231 -0.15082289 42 -6.265901 -1.6752524
## X170 -1.7280531 -0.08338161 51 -5.083206 -1.4130880
## X171 -2.1516047 0.47000363 43 -4.961845 -1.0189283
## X172 -1.6752524 -0.06187540 74 -7.323271 -1.3063602
## X174 -1.8452133 0.09531018 33 -6.265901 -1.3063602
## X175 -1.8774117 0.40546511 42 -5.149897 -1.4130880
## X176 -2.1516047 0.18232156 51 -5.472671 -1.3063602
## X177 -1.9829118 0.26236426 42 -6.319969 -1.3063602
## X178 -1.5787229 -0.31471074 65 -5.221356 -1.5342758
## X179 -1.6752524 0.47000363 42 -4.342806 -1.3405665
## X180 -2.0458231 0.91629073 42 -6.214608 -1.2415199
## X181 -1.6752524 -0.09431068 75 -7.047017 -1.5342758
## X182 -1.9981504 -0.04082199 60 -5.572754 -1.0189283
## X183 -1.7280531 -0.24846136 41 -7.094085 -1.2107086
## X184 -1.8774117 0.33647224 47 -6.265901 -1.2733760
## X185 -2.3867382 0.26236426 65 -6.938214 -1.2733760
## X186 -2.2007445 -0.06187540 42 -8.016418 -1.4919984
## X189 -1.9606157 0.26236426 51 -4.268698 -1.2415199
## X190 -2.5370840 0.47000363 47 -4.342806 -1.2733760
## X191 -1.6752524 0.18232156 61 -6.214608 -1.2415199
## X192 -1.8452133 0.64185389 61 -6.645391 -1.1519318
## X193 -2.3867382 0.58778666 43 -7.094085 -1.2107086
## X194 -1.8452133 -0.04082199 52 -6.907755 -1.3761017
## X195 -1.6256074 0.64185389 53 -5.599422 -1.1238408
## X197 -1.8774117 0.33647224 42 -5.843045 -1.1808680
## X198 -1.6752524 -0.10536052 75 -6.437752 -1.2107086
## X200 -1.8452133 0.33647224 51 -6.165818 -0.9943519
## X201 -2.2007445 0.33647224 61 -5.403678 -1.4516659
## X202 -2.2319708 -0.23572233 67 -5.952244 -1.3063602
## X205 -1.6256074 0.18232156 46 -6.502290 -1.2733760
## X208 -2.5370840 0.47000363 42 -4.744432 -0.9943519
## X210 -1.1808680 0.33647224 42 -8.111728 -1.1238408
## X212 -1.9679750 -0.12783337 51 -7.047017 -1.5787229
## X213 -1.7844998 0.18232156 59 -5.654992 -1.4130880
## X214 -2.2007445 0.26236426 53 -3.381395 -1.0189283
## X215 -1.6256074 -0.22314355 67 -5.572754 -0.9469346
## X216 -1.6752524 -0.08338161 42 -5.099467 -1.5342758
## X218 -1.8973873 -0.06187540 42 -4.744432 -1.4516659
## X219 -1.9679750 0.09531018 58 -5.426151 -1.5342758
## X220 -2.2535986 0.18232156 51 -4.791500 -1.3063602
## X223 -1.9041616 0.18232156 60 -6.907755 -1.0965412
## X224 -2.2319708 0.18232156 41 -6.265901 -1.3063602
## X225 -1.5787229 0.40546511 67 -6.571283 -0.6472472
## X226 -2.0794020 0.64185389 29 -3.688879 -1.2107086
## X227 -0.9240345 0.09531018 60 -3.611918 -1.0965412
## X228 -1.6752524 -0.17435339 67 -4.199705 -1.5787229
## X229 -2.0458231 -0.27443685 51 -8.180721 -1.5787229
## X230 -2.2646732 0.18232156 51 -4.268698 -1.2107086
## X231 -1.7844998 0.26236426 51 -5.665263 -1.5787229
## X232 -1.9829118 0.09531018 52 -6.812445 -1.2733760
## X233 -1.8452133 -0.19845094 52 -7.293418 -1.5787229
## X234 -1.8774117 -0.12783337 51 -8.377431 -1.4516659
## X236 -1.3063602 -0.17435339 32 -7.182192 -1.5342758
## X237 -1.9679750 -0.01005034 51 -5.744604 -1.2107086
## X239 -1.9981504 -0.30110509 51 -5.240048 -1.4919984
## X240 -1.5787229 -0.32850407 67 -7.641724 -1.4130880
## X241 -2.0458231 0.18232156 32 -5.051457 -1.2415199
## X242 -1.1519318 -0.06187540 46 -3.863233 -1.6256074
## X243 -2.3867382 -0.21072103 43 -5.683980 -1.3761017
## X244 -1.8452133 0.40546511 58 -6.502290 -1.1519318
## X245 -1.3063602 -0.49429632 82 -5.744604 -1.8452133
## X246 -1.9981504 0.33647224 41 -5.521461 -1.0699846
## X247 -1.4130880 -0.18632958 82 -7.130899 -1.4130880
## X249 -1.8774117 0.47000363 66 -7.013116 -0.9016377
## X250 -2.2873761 0.33647224 53 -5.496768 -0.9469346
## X251 -2.1516047 0.09531018 58 -7.621105 -1.0965412
## X253 -1.9679750 0.40546511 51 -3.057608 -1.3405665
## X254 -1.1238408 0.99325177 67 -3.912023 -0.5474623
## X255 -2.7611525 0.40546511 43 -4.422849 -1.0965412
## X256 -1.8906716 -0.23572233 58 -8.517193 -1.4919984
## X257 -1.8452133 -0.26136476 51 -6.319969 -1.5787229
## X258 -1.5342758 0.53062825 46 -4.840893 -0.8582591
## X260 -1.8973873 0.18232156 68 -5.381699 -1.3761017
## X261 -1.6752524 -0.16251893 58 -5.083206 -1.4919984
## X262 -1.3063602 -0.28768207 65 -7.641724 -1.4919984
## X263 -1.6752524 0.40546511 42 -4.509860 -1.1808680
## X264 -1.9679750 0.18232156 43 -5.368806 -1.0699846
## X265 -1.6752524 0.26236426 42 -6.812445 -1.0965412
## X267 -1.8774117 0.47000363 51 -7.323271 -1.0189283
## X268 -1.8452133 0.18232156 56 -6.032287 -1.1238408
## X269 -1.8452133 -0.15082289 41 -6.571283 -1.0699846
## X270 -1.5787229 0.69314718 52 -7.581100 -1.0441270
## X271 -2.2007445 0.47000363 52 -6.165818 -1.2415199
## X272 -1.8452133 -0.05129329 61 -5.115996 -1.1519318
## X273 -1.6752524 0.47000363 52 -6.165818 -1.3761017
## X274 -1.6752524 0.18232156 47 -5.472671 -1.3405665
## X275 -1.9981504 0.26236426 41 -3.863233 -1.1238408
## X277 -1.6256074 0.18232156 59 -4.422849 -1.6256074
## X278 -2.2319708 0.18232156 67 -4.755993 -1.1238408
## X279 -1.7844998 0.09531018 51 -6.502290 -1.5787229
## X281 -1.4919984 0.18232156 51 -7.435388 -1.3761017
## X282 -2.2873761 -0.16251893 60 -4.074542 -1.5342758
## X283 -1.6752524 0.69314718 52 -7.452482 -1.0699846
## X287 -1.8906716 0.09531018 60 -6.645391 -1.3063602
## X289 -1.3063602 -0.32850407 41 -7.849364 -1.3063602
## X290 -2.0624206 0.26236426 46 -5.426151 -1.4919984
## X291 -1.8774117 0.09531018 42 -5.203007 -1.1238408
## X292 -1.5787229 0.58778666 71 -5.521461 -0.8582591
## X294 -2.2319708 -0.40047757 51 -5.449140 -1.4130880
## X297 -2.1516047 0.18232156 62 -5.083206 -1.2733760
## X298 -1.9679750 0.09531018 37 -4.990833 -1.4919984
## X299 -1.6256074 0.40546511 51 -7.505592 -1.2733760
## X301 -1.6752524 -0.17435339 33 -5.776353 -1.2733760
## X302 -1.5787229 -0.54472718 41 -5.713833 -1.6256074
## X303 -2.0794020 0.09531018 52 -7.323271 -1.4919984
## X304 -1.6752524 0.40546511 38 -7.706263 -0.9943519
## X305 -1.6752524 0.47000363 61 -6.571283 -1.0965412
## X306 -1.6752524 0.47000363 51 -6.812445 -0.7380092
## X307 -1.4516659 0.33647224 60 -5.099467 -1.1808680
## X308 -2.2319708 0.26236426 41 -5.381699 -1.0965412
## X311 -1.8452133 0.18232156 58 -5.203007 -1.2415199
## X312 -2.7611525 0.53062825 51 -6.319969 -1.2415199
## X313 -2.2007445 0.18232156 42 -5.878136 -1.2415199
## X314 -1.6752524 0.09531018 42 -6.265901 -1.4130880
## X315 -1.8452133 0.09531018 60 -3.244194 -1.1519318
## X316 -2.2873761 0.09531018 37 -6.377127 -1.3761017
## X317 -2.0624206 0.26236426 37 -5.952244 -1.0189283
## X320 -1.7280531 0.09531018 46 -7.047017 -1.2415199
## X321 -2.0624206 -0.44628710 49 -7.293418 -1.6256074
## X322 -1.4516659 0.47000363 43 -5.099467 -1.3405665
## X323 -1.6752524 0.64185389 42 -5.843045 -1.2733760
## X324 -1.8452133 0.18232156 60 -7.169120 -1.1519318
## X325 -2.2007445 0.87546874 42 -4.906275 -1.1519318
## X326 -1.8452133 0.33647224 61 -6.502290 -1.2415199
## X327 -1.8774117 0.18232156 51 -5.020686 -1.2415199
## X329 -1.8452133 0.09531018 33 -5.776353 -1.2415199
## X330 -1.4130880 -0.05129329 42 -5.744604 -1.5342758
## X331 -2.2007445 -0.30110509 52 -7.728736 -1.4919984
## X332 -1.8452133 -0.12783337 61 -4.199705 -1.5342758
## X333 -1.6752524 0.53062825 61 -7.824046 -0.9469346
## CD5L Calbindin Calcitonin CgA Clusterin_Apo_J Complement_3
## X1 0.09531018 33.21363 1.3862944 397.6536 3.555348 -10.363053
## X2 -0.67334455 25.27636 3.6109179 465.6759 3.044522 -16.108237
## X3 0.09531018 22.16609 2.1162555 347.8639 2.772589 -16.108237
## X5 0.36331197 21.83275 1.3083328 442.8046 3.044522 -12.813142
## X6 0.40546511 13.23155 1.6292405 137.9473 2.564949 -11.983227
## X7 -0.24846136 27.12044 1.0986123 336.9532 3.178054 -16.545310
## X8 0.53062825 10.96148 1.7404662 166.5501 2.772589 -14.406260
## X9 -0.75502258 18.29778 1.2809338 254.4822 2.564949 -19.247713
## X11 -0.01005034 29.36877 2.6390573 361.5826 3.091042 -11.838035
## X12 0.83290912 20.36068 1.2809338 251.8879 2.833213 -15.709974
## X14 0.26236426 24.38181 1.8870696 270.1389 2.890372 -12.134638
## X16 -0.26136476 26.42534 0.6931472 372.6204 3.044522 -12.721137
## X17 0.33647224 22.97999 3.1354942 200.9777 2.944439 -12.813142
## X18 -0.16251893 18.39608 0.4700036 195.9948 2.302585 -17.860668
## X19 0.00000000 19.26029 1.2237754 183.6299 2.397895 -16.780588
## X20 -0.05129329 19.44761 1.6863990 367.0946 2.708050 -15.523564
## X21 0.18232156 24.07681 3.2580965 251.8879 2.484907 -13.528896
## X22 -0.86750057 22.57641 1.6486586 437.1170 2.944439 -16.108237
## X23 -0.61618614 26.14249 1.7749524 414.4903 2.564949 -18.506668
## X24 -0.75502258 24.22975 1.0296194 367.0946 3.401197 -17.860668
## X25 -0.52763274 19.44761 0.1823216 320.6970 2.708050 -20.111728
## X26 -0.63487827 23.84570 2.3978953 408.8652 2.995732 -17.860668
## X28 0.18232156 22.00000 0.6931472 288.5972 3.332205 -16.321511
## X29 -0.15082289 20.62742 0.9162907 183.6299 2.564949 -17.860668
## X30 -0.38566248 14.97056 0.7884574 304.5768 2.564949 -18.863805
## X31 0.33647224 21.83275 1.8718022 262.2911 2.995732 -16.108237
## X34 -0.16251893 24.98148 1.1314021 198.4836 2.772589 -16.108237
## X35 0.18232156 17.07878 0.6931472 259.6838 2.484907 -16.545310
## X36 0.87546874 17.49359 2.2823824 367.0946 2.833213 -13.881545
## X37 -0.57981850 23.05993 2.2823824 400.4516 3.044522 -17.860668
## X38 -0.75502258 20.18107 0.9555114 397.6536 2.708050 -16.545310
## X39 -0.32850407 18.39608 2.0412203 364.3368 3.295837 -16.545310
## X40 0.00000000 18.78461 3.2958369 361.5826 2.833213 -14.548755
## X41 0.47000363 18.68816 1.1314021 307.2539 2.890372 -9.562842
## X42 -0.77652879 22.08319 2.0668628 331.5196 2.890372 -14.696346
## X43 0.18232156 18.88061 0.9555114 231.2963 2.397895 -18.506668
## X44 0.53062825 21.49468 2.8903718 288.5972 2.995732 -12.134638
## X45 0.00000000 28.59412 3.1354942 394.8588 3.218876 -12.212827
## X46 -0.54472718 24.60827 1.9315214 420.1282 2.772589 -14.406260
## X47 0.26236426 20.09072 2.2925348 312.6196 2.564949 -13.528896
## X48 -0.17435339 30.61901 2.6390573 477.1827 3.496508 -11.909882
## X50 0.18232156 27.59730 1.6486586 336.9532 3.496508 -12.631361
## X51 -0.34249031 25.56810 0.6418539 394.8588 2.833213 -14.696346
## X53 0.18232156 23.37716 2.2823824 301.9036 2.890372 -12.374500
## X55 -0.26136476 24.07681 0.7419373 378.1598 2.833213 -15.709974
## X56 0.47000363 29.24100 1.5686159 267.5187 2.772589 -15.709974
## X57 0.18232156 26.56571 -0.1508229 315.3083 3.332205 -11.191436
## X59 0.33647224 21.40940 1.4816045 262.2911 2.890372 -12.721137
## X60 0.26236426 20.71563 0.7884574 198.4836 2.772589 -13.004247
## X61 -0.35667494 22.81935 1.6863990 195.9948 2.772589 -18.506668
## X62 -0.38566248 22.00000 0.9555114 285.9479 2.708050 -20.111728
## X63 -0.65392647 28.13304 2.7725887 356.0845 2.639057 -12.374500
## X64 0.78845736 20.36068 2.8903718 218.5779 2.890372 -13.004247
## X65 -0.41551544 20.89105 1.8870696 251.8879 2.708050 -17.860668
## X67 -0.23572233 25.92848 2.6390573 448.5044 2.833213 -14.406260
## X68 -0.59783700 20.44994 3.3672958 328.8084 2.890372 -12.631361
## X69 -0.04082199 17.39072 1.3083328 259.6838 2.302585 -14.696346
## X70 -0.96758403 24.30589 2.2407097 397.6536 3.044522 -17.028429
## X71 0.18232156 24.22975 3.5835189 296.5691 2.639057 -16.545310
## X72 0.91629073 20.44994 3.5553481 523.6660 3.401197 -13.746698
## X73 1.09861229 25.71281 1.8870696 389.2792 2.944439 -12.212827
## X74 -0.19845094 19.44761 2.3978953 149.7563 2.397895 -14.006447
## X75 -0.13926207 19.26029 1.4586150 241.5557 2.944439 -14.135373
## X76 0.00000000 27.73214 2.7080502 425.7786 3.295837 -15.008176
## X77 0.53062825 27.39388 0.9162907 315.3083 3.044522 -15.344812
## X78 0.64185389 24.38181 0.9162907 400.4516 3.332205 -13.205557
## X80 -0.82098055 21.15167 3.1780538 400.4516 2.772589 -10.909311
## X81 -0.44628710 28.13304 1.2809338 301.9036 2.944439 -17.860668
## X82 0.18232156 29.04835 3.2188758 334.2346 3.135494 -14.696346
## X83 0.33647224 19.90890 1.2237754 254.4822 2.772589 -15.523564
## X84 -0.31471074 24.15339 2.1860513 293.9078 2.708050 -15.344812
## X85 -0.24846136 27.46184 0.4700036 397.6536 3.401197 -14.849365
## X86 0.69314718 27.46184 0.6931472 336.9532 3.295837 -14.696346
## X88 0.69314718 22.81935 1.3862944 356.0845 3.135494 -10.599937
## X90 -0.44628710 13.62050 1.2237754 173.8361 2.151762 -19.662161
## X93 0.33647224 23.21904 2.6390573 246.7128 3.091042 -15.904641
## X94 0.58778666 28.33150 0.9555114 480.0666 3.367296 -13.418078
## X95 -0.21072103 26.56571 2.0794415 238.9839 2.833213 -17.860668
## X96 -0.19845094 21.15167 1.7578579 397.6536 3.295837 -16.321511
## X97 -0.52763274 29.74902 -0.7133499 375.3884 3.091042 -18.863805
## X98 -0.40047757 22.08319 2.1162555 254.4822 2.484907 -17.860668
## X99 0.09531018 20.89105 2.8332133 480.0666 2.833213 -16.545310
## X100 -0.24846136 23.61250 3.6375862 339.6754 3.091042 -16.780588
## X103 -0.07257069 22.49490 1.5686159 442.8046 3.044522 -16.545310
## X104 0.00000000 20.36068 1.5475625 389.2792 2.890372 -16.321511
## X105 -0.49429632 27.25748 0.7419373 408.8652 3.178054 -17.566939
## X107 0.00000000 24.53300 2.4849066 417.3077 2.944439 -11.909882
## X108 0.26236426 17.39072 2.0668628 403.2529 2.995732 -15.008176
## X109 -0.84397007 20.89105 1.1314021 288.5972 2.639057 -20.602047
## X110 -0.49429632 19.16601 1.3083328 223.6509 2.302585 -23.387329
## X111 -0.22314355 20.71563 2.5649494 345.1308 3.091042 -13.310348
## X112 1.16315081 24.68333 0.9555114 345.1308 3.295837 -11.983227
## X113 0.47000363 24.83282 1.5892352 275.3919 3.218876 -11.191436
## X114 -0.11653382 17.89975 1.2527630 259.6838 2.484907 -16.108237
## X115 0.47000363 27.52965 1.9878743 283.3028 2.833213 -11.698625
## X117 -0.24846136 26.21347 2.3025851 442.8046 3.135494 -18.173167
## X118 -0.24846136 27.32576 3.8918203 468.5482 3.367296 -12.543721
## X121 0.09531018 21.57965 2.0668628 425.7786 2.944439 -13.760451
## X123 -0.30110509 20.00000 2.3025851 251.8879 2.397895 -19.662161
## X124 0.26236426 22.24871 3.3672958 356.0845 2.708050 -13.881545
## X126 -0.28768207 18.97618 0.2623643 506.1507 2.302585 -18.173167
## X128 0.18232156 28.91925 0.9555114 408.8652 2.890372 -14.268559
## X129 -0.59783700 21.49468 0.2623643 291.2505 2.708050 -19.662161
## X130 -0.59783700 32.05877 1.4816045 336.9532 2.833213 -14.548755
## X131 0.00000000 27.12044 1.1314021 372.6204 2.708050 -18.863805
## X132 0.64185389 17.07878 1.5892352 318.0008 2.890372 -15.523564
## X133 0.47000363 18.00000 1.4816045 251.8879 3.218876 -12.721137
## X134 0.64185389 25.12932 1.8718022 454.2163 3.091042 -11.075694
## X135 0.18232156 26.63564 1.7227666 468.5482 2.995732 -14.696346
## X136 0.26236426 15.43560 1.6486586 283.3028 2.890372 -15.904641
## X137 -0.34249031 18.49390 2.3978953 283.3028 2.564949 -18.863805
## X139 -0.12783337 23.37716 3.4339872 342.4013 2.564949 -15.523564
## X140 -0.05129329 21.15167 1.0986123 320.6970 3.091042 -11.838035
## X141 -0.16251893 28.91925 2.3025851 417.3077 2.995732 -14.849365
## X143 0.74193734 23.05993 0.7884574 200.9777 2.890372 -15.173178
## X144 0.33647224 20.44994 1.3083328 320.6970 2.639057 -15.344812
## X145 -0.28768207 22.16609 2.8332133 309.9348 2.708050 -19.662161
## X146 0.09531018 21.66432 1.4816045 318.0008 3.218876 -13.760451
## X147 -0.04082199 26.07134 0.6931472 425.7786 3.332205 -11.564602
## X148 -0.03045921 24.75818 1.3083328 328.8084 3.258097 -13.881545
## X149 -0.44628710 18.88061 1.8405496 275.3919 2.639057 -19.662161
## X152 0.53062825 22.33105 1.9169226 411.6762 3.583519 -10.455704
## X153 0.64185389 25.27636 1.3609766 159.3166 3.258097 -12.212827
## X154 0.00000000 23.76820 2.1747517 323.3971 2.944439 -18.173167
## X155 -0.05129329 21.23790 1.5260563 462.8066 2.639057 -14.268559
## X156 -0.26136476 18.97618 2.7725887 320.6970 3.044522 -16.321511
## X157 -0.19845094 21.49468 2.3025851 272.7633 2.772589 -16.780588
## X158 -0.94160854 22.33105 2.7725887 420.1282 2.833213 -15.173178
## X159 -0.11653382 21.66432 1.6486586 417.3077 2.484907 -18.863805
## X160 0.09531018 21.49468 1.1314021 442.8046 3.583519 -15.008176
## X161 0.69314718 24.38181 0.9555114 503.2412 2.833213 -14.006447
## X162 -0.35667494 26.21347 2.7080502 437.1170 3.044522 -15.344812
## X163 -0.56211892 22.00000 1.6094379 331.5196 2.639057 -18.506668
## X165 -0.73396918 21.66432 1.7578579 392.0674 2.833213 -16.545310
## X166 -0.63487827 22.89980 1.5686159 448.5044 2.995732 -15.344812
## X167 -0.17435339 32.29286 1.6486586 414.4903 3.332205 -16.780588
## X168 -0.19845094 22.08319 2.4849066 312.6196 3.178054 -18.173167
## X169 -0.15082289 19.16601 0.6931472 315.3083 2.639057 -17.860668
## X170 0.18232156 24.15339 3.2580965 226.1946 2.772589 -14.849365
## X171 -0.02020271 21.49468 0.6931472 431.4416 3.135494 -13.760451
## X172 -0.10536052 21.91652 0.7884574 347.8639 2.772589 -18.863805
## X174 0.69314718 19.72556 2.4849066 299.2344 2.772589 -10.317725
## X175 0.91629073 17.07878 2.0281482 350.6005 2.995732 -13.205557
## X176 -1.23787436 23.21904 2.7080502 328.8084 2.639057 -15.523564
## X177 -0.05129329 16.86796 1.4816045 361.5826 2.708050 -16.780588
## X178 -0.43078292 15.32051 0.9555114 361.5826 2.174752 -20.111728
## X179 0.58778666 21.49468 2.6390573 254.4822 2.995732 -12.721137
## X180 0.40546511 21.83275 0.2623643 309.9348 2.995732 -16.545310
## X181 -0.28768207 18.19901 1.8562980 288.5972 2.219203 -18.863805
## X182 -0.51082562 24.53300 1.9600948 403.2529 2.833213 -18.863805
## X183 -0.52763274 15.77639 0.7884574 135.6050 2.639057 -17.860668
## X184 -0.43078292 20.89105 0.6931472 434.2777 2.944439 -15.709974
## X185 -0.17435339 26.91366 2.7725887 315.3083 2.833213 -15.904641
## X186 0.00000000 22.81935 -0.7133499 323.3971 2.772589 -18.506668
## X189 -0.28768207 19.44761 2.6390573 383.7128 2.833213 -18.173167
## X190 0.09531018 20.71563 1.8562980 353.3408 2.944439 -15.008176
## X191 -0.30110509 28.98387 0.6418539 283.3028 2.890372 -17.028429
## X192 -0.06187540 28.06659 0.6418539 347.8639 3.295837 -16.108237
## X193 0.40546511 33.77709 2.7725887 358.8318 3.332205 -16.545310
## X194 -0.57981850 18.88061 1.9315214 218.5779 2.833213 -15.523564
## X195 -0.35667494 21.74868 2.7725887 485.8432 3.295837 -16.545310
## X197 -0.17435339 23.69047 2.3025851 465.6759 3.178054 -11.075694
## X198 0.00000000 20.36068 1.7404662 364.3368 2.708050 -17.290073
## X200 -0.11653382 31.34666 2.7725887 293.9078 3.295837 -15.523564
## X201 0.00000000 19.72556 0.6418539 383.7128 2.772589 -14.696346
## X202 -0.52763274 20.00000 1.2237754 228.7431 2.639057 -19.662161
## X205 0.09531018 19.72556 2.3978953 389.2792 3.258097 -18.173167
## X208 -0.40047757 24.60827 2.7725887 291.2505 3.044522 -15.344812
## X210 0.26236426 26.07134 2.8903718 326.1009 3.135494 -11.698625
## X212 -0.57981850 24.53300 0.2623643 328.8084 2.890372 -18.173167
## X213 0.09531018 22.65766 2.0668628 400.4516 3.044522 -15.173178
## X214 0.26236426 17.59592 1.1314021 372.6204 2.708050 -17.290073
## X215 -0.57981850 23.05993 0.9555114 375.3884 2.772589 -16.545310
## X216 0.00000000 18.78461 0.9162907 291.2505 2.484907 -15.523564
## X218 0.47000363 17.28730 0.6418539 251.8879 2.995732 -13.642963
## X219 -0.04082199 22.49490 1.5686159 315.3083 2.564949 -18.173167
## X220 -0.02020271 23.53429 2.7080502 434.2777 2.772589 -19.247713
## X223 0.18232156 21.74868 3.4657359 309.9348 2.944439 -15.904641
## X224 0.09531018 21.57965 0.7884574 445.6530 2.772589 -18.173167
## X225 -0.37106368 29.49603 3.1354942 532.4605 3.044522 -18.173167
## X226 -1.10866262 21.49468 0.9162907 411.6762 2.944439 -16.321511
## X227 0.35333823 17.59592 0.4700036 353.3408 3.044522 -14.006447
## X228 -0.46203546 20.89105 1.4350845 307.2539 2.484907 -18.863805
## X229 0.26236426 13.36229 2.2082744 228.7431 2.484907 -17.566939
## X230 -0.03045921 23.61250 0.9162907 259.6838 2.708050 -13.103567
## X231 -0.07257069 19.54066 1.4816045 275.3919 2.944439 -14.406260
## X232 -0.16251893 23.76820 1.4109870 275.3919 2.944439 -12.813142
## X233 -0.13926207 17.39072 1.3083328 216.0486 2.564949 -15.904641
## X234 0.26236426 19.72556 1.2527630 361.5826 2.890372 -13.310348
## X236 -1.04982212 22.65766 1.7578579 383.7128 2.772589 -18.863805
## X237 0.33647224 21.40940 3.0445224 378.1598 2.995732 -16.108237
## X239 -0.17435339 11.26650 2.2721259 164.1330 1.960095 -20.602047
## X240 -0.11653382 19.81742 1.0986123 304.5768 2.564949 -14.548755
## X241 -0.44628710 23.37716 1.2527630 372.6204 2.890372 -16.108237
## X242 0.99325177 11.56466 1.7227666 149.7563 3.044522 -14.006447
## X243 -0.08338161 23.05993 1.9600948 270.1389 2.564949 -14.696346
## X244 0.09531018 28.98387 0.6931472 468.5482 2.772589 -17.028429
## X245 -1.17118298 15.32051 0.9162907 171.4017 1.871802 -20.602047
## X246 0.58778666 21.91652 -0.2357223 296.5691 2.833213 -15.173178
## X247 -0.51082562 22.33105 1.5475625 328.8084 2.484907 -19.247713
## X249 -0.37106368 25.27636 1.6677068 532.4605 3.178054 -15.904641
## X250 0.18232156 27.05168 1.5686159 380.9346 2.772589 -16.321511
## X251 -0.19845094 22.97999 0.9555114 339.6754 2.708050 -17.566939
## X253 0.33647224 19.72556 1.7749524 315.3083 2.890372 -14.406260
## X254 0.26236426 28.06659 1.8718022 462.8066 3.295837 -15.904641
## X255 0.40546511 22.97999 0.9555114 451.3589 3.091042 -14.135373
## X256 -0.18632958 24.00000 0.6931472 369.8558 2.708050 -19.662161
## X257 -0.30110509 22.97999 0.9555114 285.9479 2.484907 -20.602047
## X258 0.58778666 25.78489 1.4586150 356.0845 2.772589 -16.545310
## X260 0.40546511 25.05550 1.4109870 315.3083 2.995732 -15.709974
## X261 -0.23572233 20.62742 0.2623643 293.9078 2.397895 -20.111728
## X262 -0.47803580 20.00000 2.1747517 251.8879 2.564949 -17.566939
## X263 -0.44628710 22.81935 1.8870696 386.4943 3.178054 -12.458129
## X264 0.26236426 20.80351 0.6931472 299.2344 3.178054 -13.310348
## X265 -0.06187540 24.30589 2.2300144 342.4013 2.995732 -14.849365
## X267 -0.23572233 28.98387 1.1939225 361.5826 3.496508 -14.849365
## X268 0.83290912 24.83282 1.2237754 364.3368 2.944439 -12.458129
## X269 -0.13926207 24.68333 0.4700036 315.3083 2.639057 -18.173167
## X270 0.83290912 18.49390 1.8870696 380.9346 2.708050 -14.696346
## X271 0.87546874 18.97618 2.2925348 358.8318 2.944439 -13.760451
## X272 0.09531018 24.07681 1.8870696 270.1389 2.833213 -14.006447
## X273 -0.07257069 21.40940 2.5649494 320.6970 3.135494 -14.135373
## X274 -0.47803580 24.07681 2.2617631 356.0845 2.890372 -15.173178
## X275 -0.67334455 23.92296 0.7884574 442.8046 3.044522 -14.268559
## X277 0.53062825 19.81742 0.9162907 312.6196 2.772589 -13.642963
## X278 -0.54472718 23.45584 0.8754687 361.5826 2.890372 -14.548755
## X279 -0.19845094 24.83282 2.0918641 278.0247 2.833213 -16.545310
## X281 0.00000000 19.63331 0.6931472 386.4943 2.995732 -16.780588
## X282 0.26236426 25.05550 1.3083328 254.4822 2.995732 -16.545310
## X283 -0.16251893 30.61901 1.6486586 361.5826 3.401197 -13.418078
## X287 -0.61618614 23.53429 1.9878743 392.0674 2.944439 -17.860668
## X289 -0.35667494 15.54993 0.4700036 244.1320 2.484907 -18.506668
## X290 0.00000000 19.16601 2.2823824 278.0247 2.397895 -18.863805
## X291 0.18232156 17.59592 3.4011974 414.4903 2.708050 -13.760451
## X292 -0.04082199 29.87475 1.0986123 392.0674 3.295837 -15.709974
## X294 -0.40047757 16.54724 1.0986123 200.9777 2.639057 -17.566939
## X297 -1.17118298 25.12932 2.9957323 280.6617 2.944439 -18.506668
## X298 0.09531018 16.76166 1.1314021 254.4822 2.708050 -18.506668
## X299 -0.19845094 26.84441 1.5686159 288.5972 3.044522 -14.006447
## X301 -0.35667494 20.89105 0.6931472 331.5196 2.639057 -17.860668
## X302 -0.18632958 16.33030 1.0986123 166.5501 2.230014 -17.860668
## X303 0.53062825 24.22975 2.1400662 339.6754 2.772589 -15.709974
## X304 -1.04982212 23.84570 0.6931472 482.9535 2.995732 -17.290073
## X305 -0.03045921 31.28663 2.2721259 296.5691 3.401197 -14.548755
## X306 0.33647224 19.35416 0.9555114 535.3974 2.833213 -14.406260
## X307 0.87546874 27.52965 1.3862944 480.0666 3.044522 -14.696346
## X308 0.33647224 23.84570 0.2623643 251.8879 3.091042 -11.435607
## X311 -0.09431068 17.89975 0.6931472 334.2346 2.639057 -16.780588
## X312 -0.06187540 27.05168 1.1939225 454.2163 3.044522 -15.173178
## X313 -0.28768207 21.57965 2.4849066 238.9839 2.890372 -17.028429
## X314 -0.71334989 21.66432 2.4849066 345.1308 2.833213 -18.173167
## X315 0.09531018 21.49468 2.8903718 270.1389 3.044522 -12.292758
## X316 -0.40047757 25.42262 3.0910425 339.6754 2.833213 -14.696346
## X317 -0.63487827 24.45751 2.8903718 358.8318 2.995732 -18.173167
## X320 0.40546511 18.49390 2.6390573 211.0049 2.833213 -13.881545
## X321 0.09531018 15.66352 1.9740810 137.9473 2.397895 -16.321511
## X322 0.69314718 23.92296 1.7749524 323.3971 3.332205 -14.548755
## X323 0.09531018 20.09072 1.5475625 336.9532 2.995732 -13.103567
## X324 -0.37106368 28.91925 1.3083328 293.9078 2.890372 -15.709974
## X325 -0.23572233 22.81935 1.6486586 291.2505 2.995732 -13.642963
## X326 -0.46203546 21.74868 1.8245493 315.3083 2.772589 -17.860668
## X327 -0.37106368 23.45584 -0.7133499 345.1308 3.135494 -11.133063
## X329 -0.49429632 26.21347 2.2192035 408.8652 3.091042 -17.028429
## X330 0.26236426 11.41641 0.2623643 226.1946 2.639057 -12.543721
## X331 0.09531018 12.56022 2.6390573 296.5691 2.564949 -16.780588
## X332 0.26236426 14.49242 1.8562980 152.1371 2.833213 -12.458129
## X333 0.18232156 24.68333 1.7404662 431.4416 3.465736 -14.696346
## Complement_Factor_H Connective_Tissue_Growth_Factor Cortisol
## X1 3.5737252 0.5306283 10.0
## X2 3.6000471 0.5877867 12.0
## X3 4.4745686 0.6418539 10.0
## X5 7.2451496 0.9162907 11.0
## X6 3.5737252 0.9932518 13.0
## X7 2.4079204 0.8754687 4.9
## X8 1.0483341 0.7884574 13.0
## X9 2.4079204 0.9555114 12.0
## X11 4.0237466 0.8754687 6.8
## X12 4.6844119 0.9162907 12.0
## X14 3.2434918 0.9162907 15.0
## X16 4.2548002 0.8329091 12.0
## X17 1.8959582 0.8754687 12.0
## X18 3.7545215 0.9932518 0.1
## X19 4.0237466 1.0296194 10.0
## X20 3.0971187 0.4054651 18.0
## X21 2.7857346 0.7884574 26.0
## X22 0.9269604 0.3364722 14.0
## X23 2.4440754 0.6418539 16.0
## X24 2.3343863 0.5306283 7.8
## X25 2.1427912 0.7419373 8.6
## X26 3.4666845 0.6418539 14.0
## X28 2.2969819 0.5306283 8.9
## X29 3.3286939 0.8329091 15.0
## X30 2.1820549 0.9555114 1.8
## X31 3.1563503 1.0296194 19.0
## X34 4.2548002 1.1631508 14.0
## X35 4.4745686 1.1314021 14.0
## X36 2.0627326 0.9162907 9.8
## X37 2.8501989 0.5306283 14.0
## X38 2.5502306 1.1314021 9.5
## X39 3.6000471 0.5877867 15.0
## X40 3.6521859 0.9555114 12.0
## X41 5.0785828 1.1314021 13.0
## X42 2.4440754 0.6418539 11.0
## X43 3.7797161 0.9162907 10.0
## X44 2.9135187 0.9555114 9.5
## X45 4.4745686 0.7419373 15.0
## X46 3.7291737 0.7419373 15.0
## X47 1.9805094 1.2527630 15.0
## X48 2.7530556 0.6418539 15.0
## X50 4.2548002 0.8329091 9.8
## X51 3.0971187 0.5306283 10.0
## X53 4.4745686 0.9932518 11.0
## X55 3.1856203 0.7884574 15.0
## X56 4.2548002 0.9162907 12.0
## X57 3.6780085 0.6931472 11.0
## X59 2.8819985 0.9162907 11.0
## X60 3.7545215 0.6931472 7.0
## X61 3.4937139 0.7884574 17.0
## X62 3.3005016 0.8329091 7.1
## X63 1.1067498 1.0296194 13.0
## X64 5.4441788 1.1314021 10.0
## X65 3.2721023 0.6931472 13.0
## X67 2.0219013 0.7884574 18.0
## X68 4.4745686 0.7884574 15.0
## X69 1.7191085 1.1314021 7.4
## X70 2.2969819 0.6931472 14.0
## X71 4.8854423 0.8329091 15.0
## X72 5.4441788 0.3364722 15.0
## X73 4.2548002 0.8329091 11.0
## X74 3.3286939 1.0296194 13.0
## X75 4.0237466 0.9555114 16.0
## X76 1.4308338 0.7884574 12.0
## X77 3.5472311 0.5306283 17.0
## X78 4.8854423 0.4054651 11.0
## X80 4.4745686 0.9162907 11.0
## X81 2.7200688 0.8329091 12.0
## X82 3.5472311 0.7419373 14.0
## X83 4.2548002 0.9932518 12.0
## X84 2.0219013 1.2527630 14.0
## X85 4.4745686 0.7884574 12.0
## X86 7.1138913 0.6931472 12.0
## X88 3.4666845 0.7419373 12.0
## X90 -0.8386505 1.1314021 9.9
## X93 0.9884391 0.6418539 12.0
## X94 2.2969819 0.5306283 18.0
## X95 3.4937139 0.9555114 5.9
## X96 3.7545215 0.6418539 11.0
## X97 3.7797161 0.7419373 8.2
## X98 1.8959582 0.9162907 8.3
## X99 2.7530556 0.4054651 15.0
## X100 0.7987698 0.7419373 6.5
## X103 4.0237466 0.5306283 16.0
## X104 2.9135187 0.6418539 14.0
## X105 1.1067498 0.4700036 9.0
## X107 2.9135187 0.8754687 10.0
## X108 4.8854423 0.3364722 13.0
## X109 3.0064666 0.5877867 14.0
## X110 1.0483341 0.8329091 11.0
## X111 4.2548002 0.7884574 9.1
## X112 5.6178580 0.4054651 13.0
## X113 2.1030230 0.8754687 13.0
## X114 4.0237466 0.8754687 13.0
## X115 0.7317775 0.9162907 29.0
## X117 2.7530556 0.9162907 13.0
## X118 2.7530556 0.7884574 11.0
## X121 4.4745686 0.9555114 18.0
## X123 3.6000471 1.1939225 9.5
## X124 4.6844119 0.6418539 13.0
## X126 3.4937139 1.0647107 10.0
## X128 5.4441788 0.6931472 8.9
## X129 4.4745686 0.8754687 8.4
## X130 3.7036702 0.8754687 4.0
## X131 3.7797161 0.5306283 10.0
## X132 3.3286939 0.6418539 12.0
## X133 4.0237466 0.5306283 13.0
## X134 5.2646088 0.7419373 12.0
## X135 4.0237466 0.7419373 8.7
## X136 2.7530556 0.8329091 29.0
## X137 2.2591348 0.6931472 9.7
## X139 2.4079204 0.8329091 13.0
## X140 2.9447661 0.1823216 22.0
## X141 4.6844119 0.5877867 12.0
## X143 0.7317775 1.0296194 7.1
## X144 4.0237466 0.5877867 12.0
## X145 1.5787922 0.6931472 9.8
## X146 3.4937139 0.6418539 15.0
## X147 4.2548002 0.5306283 11.0
## X148 6.4130123 0.7884574 9.7
## X149 3.7291737 0.7419373 8.9
## X152 5.4441788 0.3364722 14.0
## X153 6.4130123 1.0986123 8.8
## X154 3.7036702 0.5877867 8.9
## X155 4.2548002 0.8754687 8.1
## X156 5.4441788 0.5877867 9.0
## X157 3.4937139 0.8329091 11.0
## X158 3.5472311 0.6931472 7.0
## X159 2.8181133 0.9932518 9.3
## X160 4.0237466 0.5877867 8.5
## X161 4.6844119 0.8754687 13.0
## X162 2.0219013 0.6931472 9.1
## X163 1.7191085 0.7884574 11.0
## X165 2.1030230 0.6418539 14.0
## X166 3.3566831 0.8329091 13.0
## X167 2.4079204 0.4700036 10.0
## X168 3.7797161 0.6418539 7.7
## X169 3.7797161 0.7884574 12.0
## X170 4.4745686 0.7884574 14.0
## X171 4.0237466 1.0647107 13.0
## X172 2.1030230 0.6418539 8.5
## X174 5.2646088 1.4109870 12.0
## X175 5.6178580 0.4054651 18.0
## X176 4.4745686 1.2809338 17.0
## X177 3.3844731 0.6931472 12.0
## X178 3.5205617 1.0296194 8.3
## X179 6.8429820 1.0986123 13.0
## X180 4.0237466 0.6418539 16.0
## X181 3.4394702 0.8754687 11.0
## X182 2.4079204 0.5877867 11.0
## X183 4.6844119 0.9932518 6.5
## X184 2.1427912 0.4700036 14.0
## X185 4.8854423 0.8329091 12.0
## X186 4.0237466 0.7884574 18.0
## X189 3.5205617 0.6418539 13.0
## X190 2.2591348 0.7884574 14.0
## X191 4.0237466 0.3364722 8.1
## X192 2.5502306 0.5877867 19.0
## X193 3.0671471 0.9932518 11.0
## X194 2.6867663 0.8754687 9.5
## X195 4.0237466 0.4054651 12.0
## X197 4.6844119 0.7884574 15.0
## X198 3.6261997 0.6931472 5.2
## X200 3.2146659 0.4700036 12.0
## X201 2.6191813 1.0647107 14.0
## X202 1.5787922 0.8754687 10.0
## X205 4.2548002 0.7419373 12.0
## X208 4.2548002 0.5306283 20.0
## X210 4.2548002 0.6418539 18.0
## X212 2.6531400 0.9162907 11.0
## X213 4.6844119 0.5306283 11.0
## X214 5.2646088 0.5306283 5.5
## X215 3.0671471 1.0296194 11.0
## X216 4.6844119 0.7884574 11.0
## X218 5.2646088 0.5877867 14.0
## X219 3.7797161 1.0296194 14.0
## X220 3.0671471 0.7884574 11.0
## X223 4.4745686 0.5877867 12.0
## X224 3.5472311 0.5877867 11.0
## X225 2.3713615 0.6418539 13.0
## X226 2.3713615 0.4054651 17.0
## X227 6.7029984 0.6418539 4.8
## X228 3.0671471 0.8329091 15.0
## X229 2.5848812 0.6931472 11.0
## X230 4.6844119 0.7884574 9.4
## X231 4.2548002 0.9162907 11.0
## X232 3.7291737 0.7419373 14.0
## X233 4.2548002 0.8754687 9.0
## X234 4.4745686 0.8754687 9.8
## X236 3.0671471 0.7884574 8.1
## X237 4.6844119 0.9555114 16.0
## X239 2.9757467 0.9932518 13.0
## X240 4.0237466 0.9932518 11.0
## X241 2.7857346 1.0296194 15.0
## X242 7.6238473 0.5877867 13.0
## X243 4.2548002 0.9555114 9.1
## X244 4.0237466 1.0647107 15.0
## X245 1.4810717 0.9555114 12.0
## X246 3.6780085 0.8754687 11.0
## X247 3.0369315 0.6931472 12.0
## X249 4.0237466 0.5877867 17.0
## X250 4.4745686 0.7419373 12.0
## X251 3.4120676 0.7419373 12.0
## X253 5.0785828 0.9932518 13.0
## X254 1.9385358 0.5877867 10.0
## X255 4.4745686 0.9555114 22.0
## X256 1.8088944 0.7884574 10.0
## X257 2.4798381 0.9555114 10.0
## X258 4.6844119 0.4054651 13.0
## X260 4.6844119 0.6931472 13.0
## X261 3.4937139 1.1631508 12.0
## X262 3.6261997 0.6931472 9.9
## X263 3.2721023 0.6931472 14.0
## X264 5.4441788 0.7884574 11.0
## X265 3.4937139 0.9162907 18.0
## X267 2.9135187 0.6931472 14.0
## X268 4.0237466 0.7419373 12.0
## X269 3.1856203 0.7419373 7.8
## X270 3.5205617 0.4700036 11.0
## X271 5.0785828 0.8329091 15.0
## X272 2.7857346 0.9555114 17.0
## X273 3.7797161 0.7884574 16.0
## X274 2.1030230 0.9162907 9.0
## X275 2.6531400 0.7419373 11.0
## X277 4.4745686 0.6931472 9.7
## X278 4.4745686 0.5877867 6.9
## X279 3.6521859 0.7419373 14.0
## X281 3.0369315 0.5877867 13.0
## X282 3.7797161 0.8329091 11.0
## X283 4.0237466 0.4700036 22.0
## X287 3.4394702 0.5877867 12.0
## X289 3.4937139 0.8329091 11.0
## X290 2.5502306 0.5877867 15.0
## X291 4.2548002 0.6418539 20.0
## X292 4.8854423 0.7419373 10.0
## X294 3.6780085 0.9162907 0.1
## X297 4.0237466 0.9555114 7.6
## X298 4.2548002 0.6931472 12.0
## X299 3.6780085 0.9162907 12.0
## X301 2.8819985 0.8329091 3.4
## X302 2.5848812 0.9932518 8.6
## X303 2.6867663 0.8329091 13.0
## X304 2.2591348 0.9932518 18.0
## X305 4.0237466 0.6418539 18.0
## X306 5.0785828 0.6931472 17.0
## X307 4.0237466 0.6931472 12.0
## X308 4.4745686 0.5306283 14.0
## X311 4.2548002 1.2237754 9.0
## X312 3.0971187 0.5877867 15.0
## X313 0.5911464 0.6418539 9.1
## X314 2.6867663 0.9555114 10.0
## X315 4.8854423 0.8754687 14.0
## X316 3.4120676 0.6931472 12.0
## X317 3.7291737 0.6931472 7.4
## X320 5.9494361 0.7419373 6.4
## X321 3.1856203 0.8754687 4.3
## X322 5.0785828 0.6931472 14.0
## X323 1.1637797 0.8329091 9.9
## X324 2.0627326 0.8754687 9.5
## X325 4.6844119 0.6418539 20.0
## X326 3.2146659 0.5877867 12.0
## X327 4.4745686 0.8329091 0.1
## X329 3.3844731 0.7419373 7.1
## X330 6.7029984 1.0296194 11.0
## X331 3.7797161 0.5306283 14.0
## X332 2.9447661 0.9932518 11.0
## X333 3.7797161 0.2623643 7.2
## Creatine_Kinase_MB Cystatin_C EGF_R EN_RAGE ENA_78 Eotaxin_3
## X1 -1.710172 9.041922 -0.13545431 -3.6888795 -1.349543 53
## X2 -1.751002 9.067624 -0.37004744 -3.8167128 -1.356595 62
## X3 -1.383559 8.954157 -0.73298708 -4.7559931 -1.390672 62
## X5 -1.625834 8.977146 -0.62060338 -2.3644605 -1.339440 64
## X6 -1.671366 7.835975 -1.11122739 -3.4420194 -1.363957 57
## X7 -1.739232 8.740337 -0.69979867 -5.0672056 -1.349543 64
## X8 -1.571048 7.736307 -0.97630091 -3.7722611 -1.381639 64
## X9 -1.671366 8.357024 -0.62060338 -4.7795236 -1.371699 64
## X11 -1.751002 8.375630 -0.51749076 -3.9633163 -1.382507 82
## X12 -1.671366 8.061487 -0.69979867 -1.3093333 -1.383822 73
## X14 -1.683772 8.692826 -0.63604036 -3.8167128 -1.360233 67
## X16 -1.671366 8.326033 -0.54612169 -3.5755508 -1.378653 69
## X17 -1.871938 8.055158 -0.65167154 -3.3524072 -1.360233 76
## X18 -1.780911 8.373323 -1.13570251 -4.4228486 -1.374923 33
## X19 -1.647864 7.615791 -1.13570251 -3.7297014 -1.367775 54
## X20 -1.518336 8.696176 -0.63604036 -2.9565116 -1.363957 77
## X21 -1.671366 7.944492 -1.01923714 -3.0576077 -1.360233 64
## X22 -1.647864 8.972083 -0.34483937 -3.1235656 -1.367775 73
## X23 -1.590122 8.373323 -0.65167154 -2.4191189 -1.372498 30
## X24 -1.751002 8.765615 -0.39571116 -5.1159958 -1.389729 82
## X25 -1.724319 8.035926 -0.87620360 -3.7297014 -1.382507 82
## X26 -1.724319 8.163371 -0.57539617 -4.0173835 -1.379499 70
## X28 -1.755051 8.737132 -0.76713789 -3.6119184 -1.346117 76
## X29 -1.647864 8.019613 -1.01923714 -4.0173835 -1.367775 34
## X30 -1.710172 8.092545 -0.74993753 -3.7722611 -1.374923 43
## X31 -1.653590 8.564077 -0.97630091 -3.5755508 -1.380778 64
## X34 -1.625834 8.407378 -0.80232932 -3.8632328 -1.383822 44
## X35 -1.625834 7.965546 -0.80232932 -3.9633163 -1.378653 44
## X36 -1.671366 8.357024 -0.69979867 -3.5065579 -1.363957 64
## X37 -1.780911 8.359369 -0.43511112 -3.1010928 -1.371699 70
## X38 -1.585271 8.294050 -0.87620360 -2.9187712 -1.378653 34
## X39 -1.683772 9.268609 -0.43511112 -4.5098600 -1.374516 62
## X40 -1.590122 8.782630 -0.93510686 -3.1235656 -1.367775 62
## X41 -1.671366 8.352319 -0.82034264 -3.2701691 -1.353033 54
## X42 -1.724319 8.538955 -0.63604036 -4.3428059 -1.404087 92
## X43 -1.710172 8.055158 -1.01923714 -4.2686979 -1.363957 43
## X44 -1.590122 8.980927 -0.66750355 -2.4769385 -1.381208 72
## X45 -1.780911 8.720950 -0.66750355 -3.6119184 -1.353033 82
## X46 -1.590122 9.341369 -0.43511112 -3.4112477 -1.376981 72
## X47 -1.868851 7.791523 -1.06412706 -3.5755508 -1.392588 64
## X48 -1.780911 8.972083 -0.42185317 -4.6459922 -1.360233 96
## X50 -1.671366 8.519191 -0.33239959 -3.5404594 -1.353033 73
## X51 -1.571048 8.954157 -0.71627709 -4.9336743 -1.390672 54
## X53 -1.518336 8.730690 -0.51749076 -1.9661129 -1.376981 52
## X55 -1.590122 8.884610 -0.40872089 -3.4737681 -1.383382 30
## X56 -1.696685 8.490849 -0.69979867 -3.4420194 -1.383822 54
## X57 -1.571048 9.441452 -0.40872089 -3.5065579 -1.386052 49
## X59 -1.671366 8.438150 -0.54612169 -2.9374634 -1.383822 64
## X60 -1.653590 8.405144 -1.11122739 -3.6119184 -1.367775 53
## X61 -1.710172 8.311398 -1.08738275 -1.9661129 -1.395547 43
## X62 -1.653590 8.188689 -0.93510686 -3.5755508 -1.374923 33
## X63 -1.724319 8.218787 -0.66750355 -3.9120230 -1.389729 64
## X64 -1.671366 8.470102 -0.78459824 -2.8473123 -1.378653 54
## X65 -1.590122 8.679312 -0.60535429 -3.4420194 -1.381208 52
## X67 -1.868851 8.821732 -0.44849801 -3.5755508 -1.383822 54
## X68 -1.647864 8.919988 -0.68354345 -4.0745419 -1.363957 64
## X69 -1.653590 8.407378 -1.13570251 -5.0672056 -1.367775 43
## X70 -1.755051 8.634087 -0.60535429 -3.9633163 -1.363957 70
## X71 -1.683772 8.646466 -0.82034264 -2.9957323 -1.363957 52
## X72 -1.751002 9.694000 -0.53172814 -3.1010928 -1.367775 83
## X73 -1.459630 9.230143 -0.43511112 -2.8302178 -1.381208 83
## X74 -1.605032 7.992945 -1.06412706 -1.4271164 -1.374923 53
## X75 -1.631218 8.398410 -0.83865049 -2.1202635 -1.372498 83
## X76 -1.755051 8.929303 -0.06111597 -3.8632328 -1.396051 54
## X77 -1.751002 8.843615 -0.54612169 -4.5098600 -1.367775 44
## X78 -1.780911 8.811354 -0.46201723 -4.1997051 -1.353033 70
## X80 -1.605032 8.171882 -0.85726607 -3.5065579 -1.349543 53
## X81 -1.780911 8.656955 -0.38282097 -2.7646206 -1.383822 44
## X82 -1.871938 8.496990 -0.80232932 -3.7297014 -1.363957 70
## X83 -1.441430 8.391630 -0.83865049 -2.9187712 -1.353033 44
## X84 -1.671366 8.301522 -0.78459824 -3.6119184 -1.378653 69
## X85 -1.671366 9.203316 -0.34483937 -3.2701691 -1.378653 44
## X86 -1.724319 9.072227 -0.63604036 -3.2835831 -1.356595 78
## X88 -1.710172 8.790269 -0.56067607 -3.1700857 -1.367775 64
## X90 -1.571048 7.625595 -1.18673610 -2.7333680 -1.386052 39
## X93 -1.830294 8.634087 -0.76713789 -4.1997051 -1.367775 64
## X94 -1.724319 9.196241 -0.37004744 -2.2072749 -1.367775 83
## X95 -1.751002 8.064636 -0.78459824 -3.2188758 -1.353033 43
## X96 -1.724319 8.625150 -0.60535429 -4.1997051 -1.371699 70
## X97 -1.780911 8.681011 -0.48946700 -3.8167128 -1.402398 70
## X98 -1.710172 8.229511 -0.91510681 -1.8971200 -1.395547 33
## X99 -1.590122 9.065315 -0.50340513 -3.6496587 -1.367775 83
## X100 -1.724319 9.061840 -0.46201723 -3.1941832 -1.378653 39
## X103 -1.590122 9.546813 -0.56067607 -4.0173835 -1.367775 93
## X104 -1.830294 9.220291 -0.65167154 -3.7722611 -1.342751 44
## X105 -1.751002 9.375855 -0.46201723 -3.5065579 -1.356595 52
## X107 -1.653590 8.877661 -0.59028711 -3.2968374 -1.367775 48
## X108 -1.653590 8.895630 -0.80232932 -4.5098600 -1.367775 64
## X109 -1.552786 8.612503 -0.69979867 -3.3813948 -1.386052 41
## X110 -1.653590 8.194229 -1.04142304 -4.1351666 -1.360233 38
## X111 -1.647864 8.767173 -0.38282097 -3.7722611 -1.367775 64
## X112 -1.653590 8.987197 -0.83865049 -3.3524072 -1.342751 59
## X113 -1.821115 8.448914 -0.35738780 -4.0745419 -1.360233 70
## X114 -1.780911 8.032685 -1.08738275 -4.1997051 -1.376154 70
## X115 -1.724319 8.237479 -0.60535429 -3.9633163 -1.373705 64
## X117 -1.653590 8.706159 -0.56067607 -0.3856625 -1.363957 64
## X118 -1.755051 8.616133 -0.60535429 -4.6777409 -1.373301 95
## X121 -1.671366 8.839277 -0.50340513 -4.1351666 -1.389729 64
## X123 -1.677510 8.151910 -1.24093251 -4.1997051 -1.405242 69
## X124 -1.647864 8.767173 -0.87620360 -4.9336743 -1.373705 44
## X126 -1.671366 8.737132 -0.85726607 -3.2441936 -1.367775 59
## X128 -1.868851 8.724207 -0.60535429 -3.5755508 -1.378653 44
## X129 -1.671366 8.122668 -0.97630091 -4.1351666 -1.389729 54
## X130 -1.696685 8.294050 -0.42185317 -3.3813948 -1.373301 57
## X131 -1.590122 8.722580 -0.71627709 -3.4737681 -1.381208 52
## X132 -1.653590 8.649974 -0.68354345 -3.0576077 -1.374923 64
## X133 -1.780911 8.416267 -0.60535429 -4.6051702 -1.386052 82
## X134 -1.631218 8.669056 -0.46201723 -1.5141277 -1.371699 70
## X135 -1.710172 8.760923 -0.71627709 -4.9198809 -1.395547 64
## X136 -1.647864 8.328451 -0.66750355 -4.1997051 -1.377815 64
## X137 -1.747018 8.143227 -0.83865049 -3.5404594 -1.390672 41
## X139 -1.671366 8.294050 -0.82034264 -3.1465552 -1.382507 57
## X140 -1.871938 8.674197 -0.59028711 -4.1997051 -1.376154 70
## X141 -1.871938 8.422883 -0.47567232 -3.1941832 -1.360233 70
## X143 -1.653590 8.266164 -1.01923714 -2.7968814 -1.360233 64
## X144 -1.751002 8.242756 -1.01923714 -4.6051702 -1.382507 64
## X145 -1.710172 8.760923 -0.89547834 -4.0173835 -1.363957 33
## X146 -1.631218 8.558335 -0.85726607 -2.6450754 -1.371699 88
## X147 -1.755051 8.738735 -0.74993753 -4.4228486 -1.360233 70
## X148 -1.571048 8.582981 -0.66750355 -2.8647040 -1.373705 73
## X149 -1.518336 8.625150 -0.71627709 -1.6607312 -1.386052 52
## X152 -1.724319 9.096051 -0.39571116 -3.6496587 -1.371699 107
## X153 -1.724319 8.474286 -0.66750355 -2.3538784 -1.371699 95
## X154 -1.518336 8.759355 -0.83865049 -3.1700857 -1.386052 62
## X155 -1.605032 8.509161 -1.08738275 -3.3813948 -1.363957 59
## X156 -1.631218 9.002085 -0.47567232 -2.3227878 -1.372498 67
## X157 -1.653590 8.887376 -0.89547834 -3.2441936 -1.367775 85
## X158 -1.590122 8.865029 -0.60535429 -4.0745419 -1.386052 62
## X159 -1.585271 7.933797 -0.59028711 -3.9633163 -1.383822 23
## X160 -1.683772 9.341369 -0.46201723 -4.4228486 -1.390672 62
## X161 -1.671366 8.887376 -0.56067607 -3.4112477 -1.367775 64
## X162 -1.647864 8.930626 -0.59028711 -3.7722611 -1.377815 49
## X163 -1.647864 8.501064 -0.60535429 -3.7722611 -1.381639 34
## X165 -1.653590 8.677610 -0.76713789 -3.8167128 -1.371699 57
## X166 -1.868851 8.649974 -0.59028711 -3.1465552 -1.389729 64
## X167 -1.647864 9.694000 -0.28367882 -4.9336743 -1.373705 64
## X168 -1.683772 8.894259 -0.39571116 -2.8824036 -1.376981 46
## X169 -1.631218 8.174703 -0.91510681 -4.8665350 -1.382507 70
## X170 -1.724319 7.922986 -0.99753895 -2.9957323 -1.349543 82
## X171 -1.671366 9.014325 -0.47567232 -4.2686979 -1.373705 54
## X172 -1.677510 8.692826 -0.91510681 -4.0745419 -1.405242 43
## X174 -1.625834 8.684401 -0.47567232 -4.4228486 -1.376154 54
## X175 -1.724319 8.503094 -0.76713789 -4.0745419 -1.371699 70
## X176 -1.671366 8.511175 -0.87620360 -1.6094379 -1.383822 64
## X177 -1.647864 8.776476 -0.68354345 -5.0832060 -1.363957 44
## X178 -1.724319 8.154788 -0.97630091 -0.4155154 -1.346117 34
## X179 -1.671366 8.188689 -0.71627709 -3.2968374 -1.376154 70
## X180 -1.653590 8.357024 -0.74993753 -4.6777409 -1.376154 82
## X181 -1.478464 8.347590 -1.04142304 -5.0832060 -1.367775 29
## X182 -1.710172 8.837826 -0.74993753 -3.1941832 -1.367775 64
## X183 -1.653590 8.048788 -1.26939244 -4.3428059 -1.360233 64
## X184 -1.590122 8.692826 -0.48946700 -2.9957323 -1.367775 70
## X185 -1.585271 8.180321 -0.69979867 -4.1351666 -1.373705 59
## X186 -1.724319 8.242756 -0.68354345 -4.1351666 -1.376154 45
## X189 -1.653590 8.345218 -0.87620360 -4.1997051 -1.382507 33
## X190 -1.647864 8.840725 -0.48946700 -4.7559931 -1.381639 54
## X191 -1.647864 8.846497 -0.76713789 -3.2968374 -1.360233 44
## X192 -1.441430 9.433484 -0.56067607 -4.4228486 -1.353033 54
## X193 -1.671366 9.002085 -0.59028711 -3.6496587 -1.389729 73
## X194 -1.647864 8.472196 -0.74993753 -3.7722611 -1.353033 44
## X195 -1.751002 9.367344 -0.32006598 -3.0791139 -1.367775 67
## X197 -1.780911 8.774931 -0.65167154 -3.5065579 -1.353033 82
## X198 -1.518336 8.478452 -0.83865049 -4.4228486 -1.363957 44
## X200 -1.653590 8.565983 -0.65167154 -4.7444323 -1.367775 43
## X201 -1.605032 8.470102 -0.83865049 -2.6882476 -1.373705 44
## X202 -1.710172 8.323608 -1.04142304 -2.6310892 -1.367775 48
## X205 -1.751002 9.037177 -0.62060338 -4.1351666 -1.386052 77
## X208 -1.571048 8.823206 -0.56067607 -3.7297014 -1.381639 44
## X210 -1.780911 8.273847 -0.59028711 -3.0791139 -1.360233 76
## X212 -1.780911 8.224164 -0.66750355 -3.0576077 -1.378653 44
## X213 -1.590122 8.465900 -0.69979867 -4.2686979 -1.349543 70
## X214 -1.459630 9.097172 -0.91510681 -2.1202635 -1.376981 41
## X215 -1.653590 8.691146 -0.76713789 -4.2686979 -1.367775 43
## X216 -1.647864 8.064636 -0.95549787 -4.4228486 -1.360233 44
## X218 -1.543930 8.283999 -0.87620360 -3.9633163 -1.373705 54
## X219 -1.671366 8.308938 -0.68354345 -3.7722611 -1.396051 44
## X220 -1.671366 8.492900 -0.59028711 -4.0173835 -1.378653 44
## X223 -1.605032 8.558335 -0.74993753 -4.4228486 -1.363957 43
## X224 -1.710172 8.874868 -0.68354345 -3.7297014 -1.367775 43
## X225 -1.653590 9.031214 -0.44849801 -4.8665350 -1.367775 53
## X226 -1.647864 9.277999 -0.59028711 -3.0159350 -1.381639 83
## X227 -1.780911 8.488794 -0.66750355 -3.9120230 -1.374923 74
## X228 -1.653590 8.474286 -0.87620360 -4.1351666 -1.360233 33
## X229 -1.780911 8.207947 -0.91510681 -4.2686979 -1.398625 45
## X230 -1.871938 8.480529 -0.93510686 -3.3813948 -1.376154 70
## X231 -1.590122 8.407378 -0.37004744 -2.4889147 -1.360233 57
## X232 -1.571048 8.785692 -0.73298708 -4.5098600 -1.363957 54
## X233 -1.751002 7.989560 -0.99753895 -4.2686979 -1.381639 34
## X234 -1.448638 8.420682 -0.74993753 -3.9120230 -1.360233 70
## X236 -1.724319 8.318742 -0.68354345 -4.7676891 -1.363957 57
## X237 -1.724319 8.765615 -0.78459824 -1.7147984 -1.367775 44
## X239 -1.605032 7.714231 -1.36134745 -2.8473123 -1.363957 33
## X240 -1.557281 8.345218 -1.01923714 -4.2686979 -1.374923 43
## X241 -1.780911 8.361708 -0.54612169 -3.2968374 -1.360233 45
## X242 -1.518336 8.306472 -0.78459824 -3.8632328 -1.363957 72
## X243 -1.625834 8.177516 -0.89547834 -2.5010360 -1.386959 44
## X244 -1.671366 8.433812 -0.74993753 -3.9120230 -1.373705 64
## X245 -1.571048 7.432484 -0.97630091 -4.2686979 -1.375743 23
## X246 -1.710172 8.499029 -0.78459824 -2.3025851 -1.367775 64
## X247 -1.751002 8.405144 -0.89547834 -3.1700857 -1.377815 39
## X249 -1.724319 9.341369 -0.48946700 -4.1351666 -1.376154 82
## X250 -1.518336 8.812843 -0.63604036 -3.4737681 -1.367775 72
## X251 -1.625834 8.743532 -0.73298708 -4.1351666 -1.378653 54
## X253 -1.724319 8.546752 -0.93510686 -3.1010928 -1.373705 64
## X254 -1.710172 9.016756 -0.19077873 -3.4737681 -1.356595 80
## X255 -1.671366 8.706159 -0.46201723 -1.8325815 -1.378653 73
## X256 -1.724319 8.550628 -0.66750355 -4.2686979 -1.378653 34
## X257 -1.625834 8.131531 -0.93510686 -4.2686979 -1.383822 44
## X258 -1.590122 9.187072 -0.68354345 -3.4420194 -1.381208 72
## X260 -1.830294 9.014325 -0.59028711 -4.6051702 -1.375743 64
## X261 -1.647864 7.955074 -0.69979867 -3.4737681 -1.367775 7
## X262 -1.571048 8.328451 -0.71627709 -3.9633163 -1.381639 39
## X263 -1.830294 8.905173 -0.51749076 -4.1997051 -1.377815 64
## X264 -1.724319 8.371011 -0.73298708 -3.4112477 -1.356595 69
## X265 -1.830294 8.896999 -0.73298708 -4.8158912 -1.373705 54
## X267 -1.751002 8.999619 -0.44849801 -3.4420194 -1.371699 70
## X268 -1.571048 8.767173 -0.47567232 -4.4228486 -1.360233 44
## X269 -1.653590 8.391630 -0.99753895 -3.5755508 -1.363957 33
## X270 -1.647864 9.546813 -0.69979867 -3.7297014 -1.386052 49
## X271 -1.571048 9.077951 -0.44849801 -3.3524072 -1.356595 64
## X272 -1.647864 8.773385 -0.68354345 -4.7559931 -1.375743 49
## X273 -1.647864 9.143132 -0.78459824 -5.2590967 -1.367775 78
## X274 -1.647864 8.400659 -0.54612169 -2.8824036 -1.386052 39
## X275 -1.780911 8.496990 -0.59028711 -5.0672056 -1.346117 53
## X277 -1.696685 8.304000 -0.74993753 -3.1941832 -1.371699 33
## X278 -1.710172 8.787220 -0.93510686 -3.7297014 -1.363957 53
## X279 -1.780911 8.271293 -0.85726607 -4.5098600 -1.353033 51
## X281 -1.631218 8.444622 -0.35738780 -2.8302178 -1.382507 82
## X282 -1.518336 8.308938 -0.83865049 -2.3751558 -1.363957 72
## X283 -1.571048 9.268609 -0.32006598 -3.3450880 -1.367775 92
## X287 -1.683772 8.794825 -0.54612169 -3.6888795 -1.376981 52
## X289 -1.605032 8.098643 -0.89547834 -4.1997051 -1.387873 33
## X290 -1.590122 8.478452 -0.93510686 -2.7646206 -1.367775 46
## X291 -1.871938 8.380227 -0.56067607 -2.6450754 -1.371699 57
## X292 -1.710172 8.771835 -0.46201723 -3.0576077 -1.387873 74
## X294 -1.557281 7.749322 -1.11122739 -3.1235656 -1.353033 43
## X297 -1.671366 8.283999 -0.57539617 -4.6777409 -1.386959 54
## X298 -1.751002 8.396155 -0.97630091 -3.0576077 -1.386052 62
## X299 -1.780911 8.610684 -0.69979867 -4.3428059 -1.389729 82
## X301 -1.671366 8.125631 -0.69979867 -4.4228486 -1.378653 54
## X302 -1.653590 7.926603 -1.13570251 -3.8167128 -1.367775 33
## X303 -1.830294 8.472196 -0.56067607 -4.0745419 -1.367775 54
## X304 -1.780911 9.143132 -0.33239959 -4.9062753 -1.383822 59
## X305 -1.647864 9.072227 -0.39571116 -3.4112477 -1.373705 73
## X306 -1.653590 9.124782 -0.71627709 -4.0173835 -1.353033 74
## X307 -1.710172 8.829080 -0.53172814 -3.3242363 -1.349543 64
## X308 -1.653590 8.648221 -0.59028711 -3.6496587 -1.360233 33
## X311 -1.724319 8.371011 -0.71627709 -3.3813948 -1.376154 54
## X312 -1.724319 8.874868 -0.37004744 -2.8302178 -1.367775 64
## X313 -1.571048 8.582981 -0.60535429 -3.5404594 -1.377815 54
## X314 -1.518336 8.662159 -0.66750355 -8.3774312 -1.386052 44
## X315 -1.710172 8.525161 -0.68354345 -3.2968374 -1.349543 48
## X316 -1.590122 8.820256 -0.62060338 -3.1235656 -1.390672 41
## X317 -1.751002 8.984694 -0.87620360 -2.3751558 -1.372498 62
## X320 -1.459630 8.470102 -0.74993753 -4.3428059 -1.363957 62
## X321 -1.590122 7.926603 -1.13570251 -3.4420194 -1.371699 52
## X322 -1.724319 8.722580 -0.47567232 -3.3242363 -1.360233 64
## X323 -1.830294 9.287301 -0.89547834 -4.8158912 -1.386052 54
## X324 -1.780911 8.398410 -0.80232932 -3.6496587 -1.339440 43
## X325 -1.571048 8.672486 -0.54612169 -4.3428059 -1.373705 54
## X326 -1.518336 8.470102 -0.97630091 -2.9374634 -1.395547 44
## X327 -1.871938 8.621553 -0.44849801 -1.9661129 -1.376154 82
## X329 -1.868851 8.588583 -0.39571116 -4.7330036 -1.371699 44
## X330 -1.780911 7.979339 -1.08738275 -4.7676891 -1.367775 70
## X331 -1.605032 8.149024 -0.68354345 -4.5098600 -1.386052 49
## X332 -1.571048 8.276395 -0.99753895 -4.1997051 -1.375743 54
## X333 -1.647864 9.694000 -0.24816638 -2.9759296 -1.373705 69
## FAS FSH_Follicle_Stimulation_Hormon Fas_Ligand
## X1 -0.08338161 -0.6516715 3.1014922
## X2 -0.52763274 -1.6272839 2.9788133
## X3 -0.63487827 -1.5630004 1.3600098
## X5 -0.12783337 -0.9763009 4.0372847
## X6 -0.32850407 -1.6832823 2.4071818
## X7 -0.71334989 -1.2988756 3.1014922
## X8 -0.71334989 -1.7833269 1.8664764
## X9 -0.82098055 -0.6053543 3.5787773
## X11 -0.02020271 -0.1795552 3.9808937
## X12 -0.71334989 -1.4294363 2.6654557
## X14 -0.44628710 -0.8023293 3.8673473
## X16 -0.41551544 -1.9018595 3.5787773
## X17 -0.02020271 -1.9376269 2.4071818
## X18 -0.82098055 -1.4294363 2.7919923
## X19 -0.47803580 -0.5034051 3.2828922
## X20 -0.63487827 -1.4660558 1.0522633
## X21 -0.07257069 -0.5753962 4.4253224
## X22 -0.30110509 -1.8460158 0.3794001
## X23 -0.86750057 -1.3613475 2.8546530
## X24 -0.07257069 -1.3946054 3.6950395
## X25 -0.57981850 -0.6997987 2.4071818
## X26 -0.16251893 -0.9975390 3.1014922
## X28 -0.49429632 -1.5208516 3.1014922
## X29 -1.04982212 -1.2988756 1.8664764
## X30 -0.96758403 -1.2409325 4.3156075
## X31 -0.82098055 -1.3294863 3.8673473
## X34 -0.82098055 -1.9987562 3.4613463
## X35 -0.82098055 -1.4660558 2.9788133
## X36 -0.49429632 -1.5586574 3.8673473
## X37 -0.28768207 -0.8203426 2.6015565
## X38 -0.82098055 -0.8762036 3.5787773
## X39 -0.44628710 -1.6779529 2.0050277
## X40 -0.63487827 -1.7397296 2.8546530
## X41 -0.52763274 -2.1151130 2.6654557
## X42 -0.61618614 -0.6053543 4.9086293
## X43 -0.57981850 -1.1867361 4.0372847
## X44 -0.63487827 -0.5753962 4.2049870
## X45 -0.05129329 -0.8954783 2.4071818
## X46 -0.44628710 -0.9975390 1.3600098
## X47 -0.75502258 -1.0641271 2.5372201
## X48 -0.30110509 -0.9351069 3.2828922
## X50 -0.44628710 -0.3448394 7.6327510
## X51 -0.65392647 -1.3294863 1.7253811
## X53 -0.52763274 -0.8023293 3.1014922
## X55 -0.63487827 -0.6206034 2.2752257
## X56 -0.71334989 -1.1867361 4.4253224
## X57 -0.11653382 -1.6520506 1.5084870
## X59 -0.71334989 -0.9554979 4.6958484
## X60 -0.82098055 -1.1357025 3.1014922
## X61 -0.71334989 -0.6516715 3.1014922
## X62 -1.10866262 -0.6997987 2.7919923
## X63 -0.37106368 -1.5942865 3.5787773
## X64 -0.44628710 -1.0641271 2.9788133
## X65 -0.73396918 -0.8023293 1.6538133
## X67 -0.44628710 -1.0641271 3.5787773
## X68 -0.08338161 -1.0192371 2.2084887
## X69 -0.96758403 -0.9151068 3.4613463
## X70 -0.44628710 -0.9554979 3.2227633
## X71 -0.52763274 -1.2134062 2.2752257
## X72 0.09531018 -1.1112274 2.6654557
## X73 -0.15082289 -0.8093644 4.8026281
## X74 -0.89159812 -1.5047321 3.1014922
## X75 -0.52763274 -0.8954783 2.2752257
## X76 -0.31471074 -0.2020847 2.6654557
## X77 -0.47803580 -0.6835434 2.2084887
## X78 0.33647224 -0.4620172 4.1493268
## X80 -0.52763274 -1.1608546 4.5882647
## X81 -0.71334989 -0.3573878 2.3414512
## X82 -0.57981850 -1.1608546 3.1014922
## X83 -0.26136476 -1.5586574 3.6950395
## X84 -0.94160854 -1.0641271 3.2828922
## X85 -0.31471074 -1.0192371 3.2828922
## X86 -0.31471074 -1.7768556 3.8673473
## X88 -0.24846136 -1.0641271 4.9613454
## X90 -0.71334989 -1.3946054 1.5084870
## X93 -0.47803580 -1.6321526 2.2084887
## X94 0.09531018 -0.8762036 3.2828922
## X95 -0.82098055 -0.9351069 3.4613463
## X96 -0.32850407 -1.3613475 2.7919923
## X97 -0.57981850 -0.6360404 3.4021763
## X98 -1.51412773 -1.3613475 1.7962595
## X99 -0.63487827 -0.8762036 0.7271504
## X100 -0.71334989 -0.9975390 2.3414512
## X103 -0.35667494 -0.1463635 2.0050277
## X104 -0.71334989 -1.4660558 3.4021763
## X105 -0.52763274 -0.5317281 1.0522633
## X107 -0.52763274 -0.6835434 1.4346609
## X108 -0.61618614 -1.4294363 2.7919923
## X109 -0.94160854 -1.2409325 2.2752257
## X110 -1.02165125 -1.3946054 3.2828922
## X111 -0.34249031 -0.6516715 1.8664764
## X112 -0.18632958 -0.9975390 3.4613463
## X113 -0.02020271 -1.3946054 4.5341630
## X114 -0.69314718 -1.5673739 2.7919923
## X115 -0.71334989 -0.6053543 3.8673473
## X117 -0.52763274 -1.9018595 3.4613463
## X118 -0.26136476 -0.8203426 3.6950395
## X121 -0.56211892 0.0971503 2.3414512
## X123 -1.10866262 -1.0414230 3.4613463
## X124 -1.04982212 -0.8203426 2.5372201
## X126 -0.61618614 -1.0641271 4.1493268
## X128 -0.31471074 -1.3294863 3.2828922
## X129 -1.07880966 -0.9151068 3.5787773
## X130 -0.07257069 -1.1608546 3.4021763
## X131 -0.57981850 -0.8386505 0.7271504
## X132 -0.71334989 -1.6622675 2.7919923
## X133 -0.32850407 -1.6726748 3.5202111
## X134 0.18232156 -0.7671379 3.4021763
## X135 -0.57981850 -1.4294363 3.1014922
## X136 -1.04982212 -0.8572661 1.1306711
## X137 -0.73396918 -1.2693924 2.1412227
## X139 -0.49429632 -0.9554979 1.7253811
## X140 -0.26136476 -1.0414230 3.9808937
## X141 -0.22314355 -0.9975390 3.9808937
## X143 -0.82098055 -1.6622675 3.7527477
## X144 -0.32850407 -2.0466943 3.6950395
## X145 -0.96758403 -0.5174908 2.4724332
## X146 -0.18632958 -1.6995924 1.7253811
## X147 -0.32850407 -1.1608546 3.9808937
## X148 -0.30110509 -0.1463635 2.5372201
## X149 -0.52763274 -0.9975390 1.6538133
## X152 0.09531018 -1.4660558 5.7312462
## X153 -0.18632958 -1.3946054 2.7919923
## X154 -0.73396918 -1.4660558 0.7271504
## X155 -0.96758403 -1.1357025 4.0372847
## X156 -0.63487827 -1.0192371 2.8546530
## X157 -0.52763274 -1.1112274 2.3414512
## X158 -0.73396918 -0.5753962 1.0522633
## X159 -0.71334989 -0.8762036 4.1493268
## X160 -0.28768207 -1.2134062 4.0934276
## X161 -0.31471074 -1.2409325 4.9086293
## X162 -0.30110509 -0.3448394 3.6950395
## X163 -1.27296568 -0.8954783 1.5084870
## X165 -0.75502258 -1.0641271 4.8026281
## X166 -0.94160854 -1.3613475 2.6654557
## X167 -0.18632958 -0.9151068 4.2049870
## X168 -0.28768207 -0.5753962 2.8546530
## X169 -0.32850407 -1.2693924 2.7919923
## X170 -0.32850407 -1.1357025 3.1014922
## X171 -0.44628710 -1.0873827 3.8673473
## X172 -0.71334989 -0.7845982 3.1014922
## X174 -0.24846136 -1.7279412 4.1493268
## X175 -0.26136476 -1.4660558 2.2752257
## X176 -0.37106368 -0.8572661 3.2828922
## X177 -0.57981850 -0.9763009 1.1306711
## X178 -0.94160854 -0.9763009 3.5787773
## X179 -0.02020271 -1.5332077 3.6950395
## X180 -0.12783337 -0.7329871 4.8026281
## X181 -0.57981850 -1.5458053 1.5084870
## X182 -0.44628710 -1.7221470 2.4724332
## X183 -0.96758403 -0.8762036 3.2828922
## X184 0.00000000 -1.7397296 2.4071818
## X185 -0.31471074 -1.0641271 2.6654557
## X186 -0.26136476 -1.5127425 0.5565448
## X189 -0.57981850 -1.0641271 2.7919923
## X190 -0.38566248 -1.3613475 2.2084887
## X191 -0.57981850 -1.5717783 2.5372201
## X192 -0.22314355 -1.6420151 4.7493371
## X193 -0.52763274 -0.7845982 4.1493268
## X194 -0.71334989 -0.7845982 2.2084887
## X195 -0.28768207 -0.4620172 3.3426940
## X197 -0.26136476 -0.3957112 3.4021763
## X198 -1.04982212 -1.1357025 3.1014922
## X200 -0.96758403 -0.5606761 4.0372847
## X201 -0.34249031 -1.5047321 0.8103017
## X202 -0.96758403 -1.2988756 2.4724332
## X205 -0.63487827 -1.3294863 3.8673473
## X208 -0.57981850 -1.2134062 2.2084887
## X210 -0.18632958 -0.7162771 4.9613454
## X212 -0.94160854 -0.9554979 2.3414512
## X213 -0.12783337 -0.9554979 3.1014922
## X214 -0.35667494 -0.9351069 0.2880017
## X215 -0.44628710 -1.4660558 3.1014922
## X216 -0.47803580 -1.9568634 2.5372201
## X218 -0.26136476 -1.7051413 3.6950395
## X219 -1.07880966 -1.0873827 2.3414512
## X220 -0.71334989 -0.8954783 2.0050277
## X223 -0.61618614 -0.5753962 2.7919923
## X224 -0.96758403 -1.5087252 2.1412227
## X225 -0.37106368 -1.0641271 4.0372847
## X226 -0.15082289 -1.1357025 2.2084887
## X227 -0.44628710 -1.3946054 4.5882647
## X228 -0.96758403 -1.4660558 3.7527477
## X229 -0.57981850 -1.7579443 2.4071818
## X230 -0.02020271 -1.0192371 2.7919923
## X231 -0.26136476 -0.9763009 3.4021763
## X232 -0.86750057 -1.8535550 0.8103017
## X233 -1.04982212 -1.1867361 1.5084870
## X234 -0.40047757 -1.2134062 2.4071818
## X236 -0.40047757 -0.9151068 3.4021763
## X237 -0.61618614 -1.6832823 2.6654557
## X239 -0.96758403 -0.7671379 2.4724332
## X240 -0.96758403 -1.3946054 3.7527477
## X241 -0.40047757 -0.9975390 3.4021763
## X242 -0.67334455 -1.5762143 2.8546530
## X243 -0.82098055 -1.2988756 2.3414512
## X244 -0.71334989 -0.9554979 2.3414512
## X245 -0.47803580 -1.7457280 1.5084870
## X246 -0.49429632 -1.3613475 2.4724332
## X247 -0.65392647 -0.8572661 3.4021763
## X249 -0.05129329 -0.8954783 3.4021763
## X250 -0.52763274 -0.8572661 1.8664764
## X251 -1.07880966 -0.6516715 2.2084887
## X253 -0.52763274 -1.1357025 2.8546530
## X254 -0.03045921 -0.6206034 5.7312462
## X255 -0.52763274 -0.4756723 2.9788133
## X256 -1.07880966 -0.9351069 2.0050277
## X257 -0.82098055 -1.2409325 3.2828922
## X258 -0.35667494 -0.4620172 2.5372201
## X260 -0.30110509 -1.3946054 2.2084887
## X261 -1.07880966 -1.3613475 4.1493268
## X262 -0.86750057 -1.5806825 3.4021763
## X263 -0.30110509 -0.8954783 2.2084887
## X264 -0.52763274 -1.2134062 3.5787773
## X265 -0.57981850 -1.4294363 2.3414512
## X267 -0.02020271 -1.2988756 3.9808937
## X268 -0.57981850 -0.9351069 4.2049870
## X269 -0.71334989 -0.9975390 2.4724332
## X270 -0.22314355 -1.6129165 3.9808937
## X271 -0.30110509 -1.1608546 3.2828922
## X272 -0.57981850 -1.6520506 2.2084887
## X273 -0.22314355 -1.3613475 3.4021763
## X274 -0.47803580 -1.6886644 2.5372201
## X275 -0.61618614 -1.5087252 2.1412227
## X277 -0.32850407 -1.3613475 3.4021763
## X278 -0.96758403 -1.5586574 4.5882647
## X279 -0.40047757 -0.8572661 2.0734090
## X281 -0.07257069 -1.2988756 3.4021763
## X282 -0.52763274 -1.2409325 0.2880017
## X283 -0.38566248 -0.5174908 4.2049870
## X287 -0.63487827 -0.9151068 1.6538133
## X289 -0.96758403 -1.2134062 4.5882647
## X290 -0.63487827 -0.6206034 0.2880017
## X291 0.00000000 -0.7845982 2.4071818
## X292 -0.37106368 -1.1112274 4.0372847
## X294 -0.82098055 -2.0738009 2.4724332
## X297 -0.94160854 -1.1357025 2.9788133
## X298 -0.94160854 -1.7833269 1.3600098
## X299 -0.40047757 -0.7162771 3.9808937
## X301 -0.94160854 -1.7338014 4.9086293
## X302 -0.96758403 -1.5047321 2.7919923
## X303 -0.38566248 -1.5332077 2.2084887
## X304 -0.31471074 -1.1608546 3.2828922
## X305 -0.30110509 -1.3946054 3.1014922
## X306 -0.24846136 -1.5673739 3.1014922
## X307 -0.37106368 -1.5047321 4.5882647
## X308 -0.96758403 -1.2409325 4.0372847
## X311 -0.71334989 -0.6675036 3.5787773
## X312 -0.52763274 -1.8460158 2.6654557
## X313 -0.71334989 -1.1112274 2.2084887
## X314 -0.38566248 -0.9554979 3.1014922
## X315 -0.61618614 -1.2693924 3.7527477
## X316 -0.57981850 -0.3573878 0.2880017
## X317 -0.52763274 -0.6053543 2.8546530
## X320 -0.44628710 -1.6622675 3.6370513
## X321 -0.86750057 -1.5208516 1.3600098
## X322 -0.41551544 -1.0414230 4.9086293
## X323 -0.15082289 -1.5047321 2.8546530
## X324 -0.71334989 -1.2409325 4.0372847
## X325 -0.47803580 -0.8954783 2.5372201
## X326 -0.71334989 -0.3957112 3.6950395
## X327 -0.02020271 -1.2988756 2.7919923
## X329 -0.61618614 -1.6082042 3.5787773
## X330 -0.26136476 -0.8954783 2.7919923
## X331 -0.71334989 -1.1112274 -0.1536154
## X332 -0.57981850 -1.8535550 3.1014922
## X333 -0.08338161 -0.5753962 3.6950395
## Fatty_Acid_Binding_Protein Ferritin Fetuin_A Fibrinogen GRO_alpha
## X1 2.52087117 3.3291650 1.2809338 -7.035589 1.381830
## X2 2.24779664 3.9329588 1.1939225 -8.047190 1.372438
## X3 0.90630094 3.1768716 1.4109870 -7.195437 1.412679
## X5 2.63458831 2.6904158 2.1517622 -6.980326 1.398431
## X6 0.62373057 1.8470768 1.4816045 -6.437752 1.398431
## X7 1.59753955 3.4405882 1.1314021 -7.621105 1.338425
## X8 0.74349177 2.8166378 1.6677068 -6.502290 1.350892
## X9 0.34805188 2.3817805 1.0647107 -7.902008 1.381830
## X11 0.62373057 3.0596443 1.4350845 -7.523941 1.412679
## X12 0.55980793 3.3291650 1.4109870 -7.278819 1.398431
## X14 1.53020362 3.0199602 1.3862944 -6.991137 1.440955
## X16 2.65289688 2.2426407 1.4816045 -7.222466 1.412679
## X17 0.49280272 2.5607017 1.7578579 -6.319969 1.419083
## X18 1.05291638 2.5607017 0.8754687 -7.402052 1.324552
## X19 0.26936976 1.7416574 1.3350011 -6.959049 1.405814
## X20 1.49546653 2.4271887 1.5260563 -5.843045 1.430692
## X21 1.14329840 2.2426407 1.3350011 -7.182192 1.398431
## X22 2.20192082 4.0663004 0.8754687 -7.385791 1.405814
## X23 1.05291638 3.0596443 1.0986123 -7.641724 1.338425
## X24 0.90630094 4.6332496 1.1631508 -7.600902 1.372438
## X25 0.49280272 2.6475800 1.0986123 -7.435388 1.308996
## X26 1.89864831 3.0199602 1.0986123 -7.452482 1.381830
## X28 2.31450540 4.3245553 1.3083328 -7.323271 1.350892
## X29 0.62373057 2.2895221 1.0986123 -7.875339 1.338425
## X30 0.26936976 2.3817805 0.9162907 -7.875339 1.398431
## X31 1.78455817 2.5166359 1.5040774 -6.645391 1.381830
## X34 1.09877705 2.0496913 1.5686159 -6.969631 1.458333
## X35 0.79981129 2.2426407 1.7749524 -7.236259 1.362172
## X36 1.45997005 0.8982753 1.8562980 -6.571283 1.445658
## X37 2.35766182 3.3291650 1.0986123 -7.505592 1.398431
## X38 1.26963623 1.6331804 1.0296194 -7.561682 1.398431
## X39 2.29253952 3.7271284 0.9932518 -8.111728 1.291400
## X40 1.56421694 2.1952354 1.4350845 -7.824046 1.430692
## X41 1.42367579 2.4721360 2.0541237 -6.377127 1.398431
## X42 1.42367579 2.6475800 1.3609766 -7.487574 1.398431
## X43 0.90630094 1.9496835 1.3083328 -7.143478 1.362172
## X44 2.15484541 2.5607017 1.4816045 -6.812445 1.362172
## X45 2.87633811 3.8991525 1.6486586 -6.571283 1.425073
## X46 2.24779664 2.9396356 0.6418539 -8.180721 1.372438
## X47 0.49280272 2.0000000 1.4109870 -7.505592 1.405814
## X48 1.95297508 3.9329588 0.7884574 -8.111728 1.405814
## X50 2.03141194 2.6904158 1.2237754 -7.354042 1.435976
## X51 2.08182149 2.9396356 1.2809338 -7.278819 1.338425
## X53 1.18656534 2.2426407 1.2809338 -6.907755 1.390462
## X55 1.45997005 3.8651513 1.1314021 -7.641724 1.372438
## X56 1.78455817 2.1472883 1.8405496 -7.208860 1.390462
## X57 1.89864831 3.7619441 1.2527630 -7.264430 1.338425
## X59 2.08182149 3.1380930 1.6094379 -7.082109 1.430692
## X60 -0.06149412 2.2895221 0.9932518 -6.812445 1.381830
## X61 0.74349177 2.4271887 1.2237754 -7.013116 1.362172
## X62 -0.81662520 0.8982753 0.9555114 -7.662778 1.324552
## X63 1.69366072 4.2928531 1.3350011 -7.250246 1.390462
## X64 1.59753955 2.7328638 1.8870696 -7.195437 1.430692
## X65 1.56421694 3.2915026 0.4700036 -8.254829 1.372438
## X67 1.05291638 3.2535702 1.0296194 -7.523941 1.390462
## X68 1.26963623 3.2153619 1.5040774 -7.278819 1.308996
## X69 0.79981129 1.2863353 1.3609766 -7.957577 1.308996
## X70 1.38654222 2.1952354 0.9162907 -7.957577 1.381830
## X71 1.72450839 3.3291650 1.4816045 -7.505592 1.350892
## X72 2.31450540 2.0987803 1.9169226 -6.505132 1.372438
## X73 2.31450540 3.4037024 1.3609766 -7.354042 1.338425
## X74 -0.41274719 2.2895221 1.5260563 -7.143478 1.362172
## X75 1.30957344 2.0000000 1.7227666 -6.437752 1.430692
## X76 2.92446596 3.4772256 1.4586150 -7.130899 1.430692
## X77 2.03141194 4.6332496 1.4109870 -6.319969 1.462144
## X78 3.21875915 2.7328638 1.4816045 -6.502290 1.445658
## X80 1.18656534 3.0990195 1.2237754 -6.917806 1.338425
## X81 1.09877705 2.0496913 1.3350011 -7.902008 1.419083
## X82 1.38654222 2.4721360 1.3350011 -7.338538 1.350892
## X83 0.95678949 3.8309519 1.4586150 -8.804875 1.372438
## X84 1.14329840 2.1952354 1.4816045 -7.250246 1.435976
## X85 0.49280272 4.0991803 1.7917595 -7.182192 1.362172
## X86 2.17853747 3.5497748 2.1162555 -6.214608 1.435976
## X88 2.08182149 3.5136195 1.4816045 -6.917806 1.430692
## X90 -1.04412698 1.2249031 0.5877867 -8.873868 1.350892
## X93 1.56421694 3.6568542 1.4350845 -6.959049 1.372438
## X94 3.70550563 3.2915026 2.1860513 -6.502290 1.475713
## X95 0.55980793 3.5136195 1.2809338 -7.469874 1.405814
## X96 1.09877705 3.2915026 0.8329091 -7.986565 1.381830
## X97 1.72450839 2.8166378 0.9555114 -7.875339 1.381830
## X98 0.42235886 2.0496913 1.0296194 -8.468403 1.324552
## X99 2.39982883 3.0596443 1.0647107 -6.812445 1.405814
## X100 1.45997005 3.0990195 1.3609766 -7.751725 1.362172
## X103 3.21875915 3.2535702 1.3083328 -7.799353 1.405814
## X104 1.56421694 3.6920998 1.1939225 -7.323271 1.372438
## X105 1.66223369 3.7619441 1.1314021 -7.706263 1.372438
## X107 1.87083027 2.6904158 1.2237754 -7.208860 1.398431
## X108 1.38654222 3.5136195 2.0014800 -7.070274 1.338425
## X109 0.68487244 2.6475800 0.6418539 -8.517193 1.350892
## X110 0.79981129 2.6904158 0.7419373 -8.334872 1.435976
## X111 1.63020224 3.9329588 1.1314021 -7.293418 1.350892
## X112 2.86003086 2.9799598 1.8870696 -6.214608 1.425073
## X113 1.95297508 3.7965507 2.1041342 -5.991465 1.390462
## X114 0.34805188 1.1622777 1.0647107 -7.035589 1.381830
## X115 0.49280272 2.3358967 1.8870696 -6.437752 1.450108
## X117 1.75480019 2.3817805 1.2527630 -7.452482 1.398431
## X118 2.52087117 3.4772256 1.2237754 -7.452482 1.435976
## X121 2.54030520 3.3665631 1.7227666 -7.250246 1.398431
## X123 -0.01004024 0.6832816 0.9162907 -8.334872 1.350892
## X124 0.26936976 2.5166359 1.7404662 -7.182192 1.338425
## X126 1.42367579 1.7416574 1.1631508 -7.195437 1.381830
## X128 0.55980793 2.3817805 1.9878743 -6.725434 1.454327
## X129 0.00000000 2.5607017 1.2527630 -7.600902 1.338425
## X130 1.26963623 2.9799598 1.6292405 -6.571283 1.372438
## X131 1.30957344 2.5166359 1.1314021 -7.418581 1.372438
## X132 1.69366072 2.7749346 1.5892352 -7.143478 1.362172
## X133 1.05291638 3.5856960 1.8082888 -5.914504 1.271288
## X134 0.68487244 3.6213877 1.6094379 -6.812445 1.398431
## X135 2.15484541 2.0496913 1.5686159 -7.250246 1.308996
## X136 1.09877705 1.6878178 1.2809338 -6.907755 1.412679
## X137 0.79981129 1.5777088 0.9555114 -7.902008 1.381830
## X139 0.90630094 3.2153619 1.0647107 -7.662778 1.338425
## X140 1.56421694 2.6475800 1.2809338 -7.250246 1.398431
## X141 1.49546653 3.2915026 1.7227666 -6.437752 1.419083
## X143 0.85402456 2.5166359 1.2527630 -7.561682 1.350892
## X144 1.49546653 3.3291650 1.2809338 -7.070274 1.372438
## X145 0.79981129 2.3817805 1.0647107 -8.180721 1.350892
## X146 1.66223369 2.8989795 1.8718022 -6.725434 1.381830
## X147 1.63020224 3.1380930 1.4816045 -7.024289 1.412679
## X148 1.14329840 2.6043458 2.0281482 -6.907755 1.425073
## X149 0.90630094 2.3358967 0.9162907 -7.875339 1.381830
## X152 2.46133172 4.1318839 1.8245493 -6.571283 1.494568
## X153 1.42367579 3.0990195 1.9459101 -8.111728 1.372438
## X154 1.38654222 3.0199602 0.7419373 -7.542634 1.308996
## X155 1.22865470 2.7328638 1.1939225 -7.986565 1.372438
## X156 1.81380304 3.3291650 1.6486586 -6.991137 1.390462
## X157 2.17853747 2.6904158 0.8754687 -7.957577 1.324552
## X158 1.14329840 2.9396356 1.0647107 -7.799353 1.350892
## X159 0.62373057 1.1622777 0.9555114 -6.917806 1.450108
## X160 2.13083532 3.7271284 1.0647107 -7.452482 1.350892
## X161 0.09622438 2.3358967 1.5892352 -7.706263 1.419083
## X162 2.13083532 3.2153619 0.6931472 -7.751725 1.405814
## X163 0.62373057 2.6043458 0.8754687 -7.561682 1.390462
## X165 2.33621055 2.9799598 0.9555114 -7.402052 1.390462
## X166 0.95678949 2.8166378 0.9555114 -7.728736 1.350892
## X167 2.10649743 3.4772256 0.9162907 -7.875339 1.372438
## X168 1.38654222 2.7328638 1.3609766 -7.418581 1.350892
## X169 0.95678949 1.7947332 1.2527630 -7.182192 1.324552
## X170 0.68487244 3.3665631 1.5475625 -7.013116 1.372438
## X171 1.53020362 2.5166359 1.8405496 -7.323271 1.381830
## X172 0.79981129 2.8166378 1.2809338 -7.323271 1.381830
## X174 1.26963623 3.4405882 2.1400662 -8.421883 1.412679
## X175 2.54030520 2.7749346 1.6094379 -5.914504 1.435976
## X176 0.95678949 2.1952354 1.4350845 -8.873868 1.419083
## X177 2.03141194 2.9799598 1.1314021 -8.016418 1.338425
## X178 0.26936976 0.6076810 1.0986123 -7.799353 1.412679
## X179 0.09622438 1.8470768 2.1633230 -7.323271 1.398431
## X180 1.89864831 2.8579831 1.2527630 -7.369791 1.398431
## X181 1.00562217 2.0000000 1.0647107 -8.254829 1.291400
## X182 0.42235886 3.2535702 0.9555114 -7.435388 1.324552
## X183 1.14329840 1.0331502 1.7227666 -6.645391 1.372438
## X184 0.68487244 2.4271887 1.0986123 -7.561682 1.372438
## X185 1.89864831 1.9496835 1.8562980 -6.812445 1.435976
## X186 1.42367579 2.6475800 1.2527630 -7.208860 1.308996
## X189 0.62373057 2.7328638 0.5306283 -7.487574 1.338425
## X190 1.49546653 3.5497748 1.1631508 -7.293418 1.291400
## X191 1.69366072 2.8989795 1.2809338 -7.338538 1.308996
## X192 2.57858283 3.3665631 1.4586150 -7.684284 1.425073
## X193 1.78455817 3.1380930 1.4350845 -7.278819 1.405814
## X194 0.26936976 3.1380930 1.5686159 -7.469874 1.308996
## X195 2.50123416 3.2153619 1.2237754 -7.523941 1.412679
## X197 1.59753955 3.3665631 1.5040774 -7.118476 1.405814
## X198 0.79981129 1.6878178 1.1631508 -7.418581 1.350892
## X200 1.18656534 3.0990195 0.9555114 -7.662778 1.372438
## X201 0.79981129 2.5607017 1.3609766 -7.561682 1.381830
## X202 0.62373057 2.6043458 0.8754687 -8.294050 1.308996
## X205 1.97951393 3.4772256 1.2809338 -7.487574 1.390462
## X208 1.05291638 3.5136195 1.9600948 -6.948577 1.291400
## X210 1.97951393 2.6043458 1.3350011 -7.106206 1.398431
## X212 1.84255429 2.2426407 1.3350011 -7.621105 1.390462
## X213 2.08182149 3.2153619 1.6486586 -6.812445 1.405814
## X214 2.24779664 2.4271887 1.4109870 -7.013116 1.462144
## X215 1.59753955 2.9799598 0.7884574 -7.824046 1.324552
## X216 0.95678949 1.6331804 1.5686159 -6.119298 1.425073
## X218 -0.12621307 1.8987177 1.7227666 -6.571283 1.398431
## X219 0.55980793 1.5777088 1.2809338 -7.684284 1.412679
## X220 0.85402456 2.4271887 0.9555114 -7.849364 1.390462
## X223 1.84255429 3.1768716 1.7578579 -6.917806 1.338425
## X224 1.26963623 2.6475800 1.2527630 -7.264430 1.308996
## X225 3.07697133 2.2895221 0.8754687 -7.418581 1.372438
## X226 2.31450540 3.4405882 1.2237754 -7.024289 1.398431
## X227 1.78455817 2.5166359 2.2512918 -6.214608 1.398431
## X228 0.49280272 1.2863353 0.7419373 -7.070274 1.362172
## X229 1.18656534 1.6878178 1.3350011 -7.621105 1.381830
## X230 1.63020224 2.1952354 1.5260563 -7.293418 1.390462
## X231 1.09877705 2.7328638 1.1939225 -6.907755 1.381830
## X232 1.00562217 2.8989795 1.5040774 -7.143478 1.362172
## X233 0.85402456 2.0987803 1.2237754 -7.469874 1.398431
## X234 2.08182149 3.0199602 1.4586150 -7.250246 1.271288
## X236 -0.17134851 2.9396356 0.9932518 -7.621105 1.324552
## X237 1.00562217 4.3245553 2.0281482 -7.182192 1.390462
## X239 0.09622438 1.0331502 0.6418539 -8.217089 1.271288
## X240 0.85402456 2.2895221 0.9162907 -8.145630 1.324552
## X241 1.53020362 3.4405882 1.7578579 -8.047190 1.338425
## X242 0.85402456 1.7416574 1.9399676 -7.600902 1.372438
## X243 0.26936976 2.8579831 1.6292405 -6.938214 1.362172
## X244 2.17853747 3.8991525 1.3862944 -7.799353 1.435976
## X245 -0.10425819 2.3817805 0.7419373 -8.740337 1.390462
## X246 1.34852439 2.5166359 1.9459101 -7.208860 1.362172
## X247 0.90630094 2.1952354 0.6931472 -8.180721 1.398431
## X249 3.21875915 4.0332413 1.0296194 -7.581100 1.398431
## X250 1.38654222 2.5607017 1.3083328 -7.600902 1.381830
## X251 0.85402456 1.6331804 1.1631508 -7.775256 1.362172
## X253 1.45997005 2.3358967 1.5260563 -6.502290 1.405814
## X254 3.07697133 4.0332413 1.9315214 -7.250246 1.398431
## X255 1.87083027 2.9396356 1.6094379 -8.740337 1.405814
## X256 1.00562217 3.5136195 0.7884574 -8.334872 1.324552
## X257 -0.05103109 2.7749346 0.9162907 -7.775256 1.362172
## X258 2.70679585 3.0596443 1.7578579 -6.948577 1.390462
## X260 1.30957344 3.3665631 1.5686159 -6.938214 1.440955
## X261 0.26936976 2.0496913 0.9555114 -8.111728 1.398431
## X262 0.09622438 3.0596443 1.2527630 -7.308233 1.390462
## X263 1.78455817 3.1380930 1.7227666 -7.561682 1.372438
## X264 1.66223369 3.0596443 1.9315214 -6.502290 1.350892
## X265 1.00562217 2.4271887 1.3609766 -7.775256 1.350892
## X267 2.27030576 3.2535702 1.3609766 -7.047017 1.390462
## X268 1.87083027 3.0990195 1.4109870 -7.600902 1.362172
## X269 0.62373057 1.7416574 0.9162907 -7.875339 1.324552
## X270 2.13083532 2.7749346 0.9932518 -7.662778 1.440955
## X271 1.84255429 4.0000000 1.5686159 -6.959049 1.398431
## X272 1.09877705 3.0199602 1.6094379 -7.824046 1.350892
## X273 1.92602476 3.6568542 1.2237754 -7.222466 1.350892
## X274 1.09877705 3.4037024 1.2237754 -7.505592 1.398431
## X275 1.66223369 2.9396356 1.6292405 -8.047190 1.338425
## X277 0.95678949 3.4405882 1.7227666 -6.725434 1.405814
## X278 1.53020362 3.6568542 1.0647107 -6.725434 1.324552
## X279 0.62373057 2.2426407 1.2527630 -7.706263 1.381830
## X281 2.15484541 3.0990195 1.2809338 -6.571283 1.398431
## X282 1.72450839 2.2426407 1.6094379 -8.740337 1.419083
## X283 1.87083027 4.6332496 1.6094379 -6.725434 1.435976
## X287 1.45997005 3.1380930 1.0647107 -7.775256 1.372438
## X289 0.42235886 2.3358967 1.0296194 -7.542634 1.338425
## X290 1.34852439 1.7947332 0.8329091 -7.824046 1.445658
## X291 1.59753955 3.3291650 1.2809338 -7.058578 1.475713
## X292 2.00565516 3.8309519 1.6292405 -7.236259 1.350892
## X294 0.42235886 1.7947332 1.2237754 -7.662778 1.308996
## X297 0.74349177 2.3817805 1.2237754 -8.016418 1.350892
## X298 1.22865470 1.7416574 1.4350845 -7.082109 1.291400
## X299 2.24779664 2.7328638 1.1314021 -7.581100 1.372438
## X301 0.79981129 1.6878178 1.1314021 -8.047190 1.362172
## X302 -0.06149412 1.4641016 1.2527630 -7.469874 1.271288
## X303 1.09877705 3.0596443 1.6677068 -7.156217 1.308996
## X304 2.74190799 2.3817805 0.9555114 -7.728736 1.445658
## X305 0.55980793 4.1644140 1.2809338 -6.980326 1.390462
## X306 2.10649743 1.4058773 1.8082888 -7.106206 1.412679
## X307 2.75922787 4.6332496 1.9315214 -5.914504 1.372438
## X308 1.69366072 2.8166378 1.6677068 -6.725434 1.381830
## X311 0.68487244 2.7328638 1.5040774 -7.600902 1.412679
## X312 2.13083532 3.8309519 1.5040774 -7.293418 1.390462
## X313 1.49546653 2.3358967 1.4586150 -7.505592 1.440955
## X314 1.05291638 2.0987803 0.8754687 -8.334872 1.324552
## X315 0.68487244 2.6475800 1.8245493 -7.024289 1.372438
## X316 1.69366072 3.2153619 1.1631508 -7.799353 1.338425
## X317 1.78455817 2.8166378 1.1631508 -7.581100 1.350892
## X320 0.26936976 2.3358967 1.9740810 -6.725434 1.308996
## X321 0.55980793 1.7947332 1.9600948 -7.728736 1.324552
## X322 1.00562217 3.2915026 1.8562980 -6.377127 1.398431
## X323 1.53020362 3.0199602 1.2809338 -7.195437 1.338425
## X324 1.26963623 2.5166359 0.4700036 -8.468403 1.271288
## X325 1.18656534 2.1952354 1.7749524 -6.907755 1.398431
## X326 1.78455817 1.8987177 0.7419373 -7.986565 1.398431
## X327 1.95297508 3.5136195 1.3609766 -7.293418 1.398431
## X329 1.53020362 2.0496913 1.1314021 -7.775256 1.405814
## X330 0.90630094 2.0000000 2.1972246 -6.571283 1.381830
## X331 0.55980793 1.6331804 1.0296194 -7.236259 1.372438
## X332 -0.38485910 2.4271887 1.3609766 -7.024289 1.362172
## X333 2.33621055 3.0596443 1.5475625 -7.236259 1.350892
## Gamma_Interferon_induced_Monokin Glutathione_S_Transferase_alpha HB_EGF
## X1 2.949822 1.0641271 6.559746
## X2 2.721793 0.8670202 8.754531
## X3 2.762231 0.8890150 7.745463
## X5 2.851987 1.2358607 7.245150
## X6 2.822442 1.1538270 6.413012
## X7 2.739315 1.1421966 6.262563
## X8 2.966101 1.0343625 6.559746
## X9 2.584357 0.9853483 9.736307
## X11 2.701785 0.9676836 8.542148
## X12 2.769220 1.0782394 6.108135
## X14 2.924402 1.0495119 7.745463
## X16 2.911527 0.7941114 8.754531
## X17 2.845167 1.1538270 6.413012
## X18 2.956388 1.1970974 5.264609
## X19 3.019718 1.0343625 4.254800
## X20 2.708297 0.8670202 6.262563
## X21 2.929867 0.9853483 7.373808
## X22 2.724975 0.7083677 9.454406
## X23 2.568127 0.8439372 6.979888
## X24 2.614139 0.8890150 7.500000
## X25 2.667835 1.0343625 5.949436
## X26 2.788951 0.8890150 9.358191
## X28 2.680311 0.9853483 7.245150
## X29 2.713850 0.9300710 6.262563
## X30 2.766469 1.2358607 3.779716
## X31 2.790112 1.2871473 4.474569
## X34 2.883453 1.1178380 5.264609
## X35 2.802914 0.8890150 7.982407
## X36 2.848747 1.0186444 6.842982
## X37 2.786199 0.9676836 7.982407
## X38 2.919789 1.0023201 6.413012
## X39 2.620513 0.8890150 6.979888
## X40 2.876049 1.0641271 5.949436
## X41 2.825646 1.1302068 8.211578
## X42 2.603403 0.7083677 7.982407
## X43 2.927719 1.2071782 5.444179
## X44 2.908388 0.7671379 7.864950
## X45 2.792403 1.0343625 9.260790
## X46 2.762231 0.7941114 8.542148
## X47 2.757357 0.8439372 6.413012
## X48 2.879640 1.1050693 6.559746
## X50 2.787386 0.9100066 9.454406
## X51 2.829324 0.7386101 7.113891
## X53 2.848276 0.9100066 7.245150
## X55 2.637144 0.7083677 8.649098
## X56 2.852215 0.8439372 7.500000
## X57 2.864362 0.7386101 5.617858
## X59 2.974175 0.7671379 7.982407
## X60 2.936028 1.0023201 4.474569
## X61 2.742679 1.1651160 4.885442
## X62 2.684529 1.1970974 5.949436
## X63 2.726228 1.0186444 6.262563
## X64 3.065368 1.0023201 7.113891
## X65 2.632564 0.7671379 7.864950
## X67 2.674201 0.7386101 7.113891
## X68 2.713850 0.8670202 5.786135
## X69 2.732330 1.0641271 4.474569
## X70 2.809013 0.9853483 7.745463
## X71 2.780093 1.0186444 6.559746
## X72 2.928799 0.9676836 9.162164
## X73 2.939917 0.9853483 7.745463
## X74 2.916177 1.2950103 3.272102
## X75 2.939917 0.9853483 6.413012
## X76 2.818539 0.8439372 9.454406
## X77 2.946345 0.8670202 5.786135
## X78 2.943646 0.9676836 7.745463
## X80 2.735867 1.1760805 6.413012
## X81 2.760303 0.9300710 7.982407
## X82 2.757852 0.9676836 6.413012
## X83 2.668760 0.9676836 6.702998
## X84 2.774554 0.9492763 6.979888
## X85 2.851530 0.9676836 7.373808
## X86 2.851530 1.0023201 8.323453
## X88 2.909446 1.1050693 5.444179
## X90 2.668760 0.6419718 2.103023
## X93 2.881737 0.6762264 6.979888
## X94 3.032417 0.7941114 9.828139
## X95 2.868085 1.3175721 6.108135
## X96 2.827923 0.8439372 9.736307
## X97 2.778414 0.9676836 8.649098
## X98 2.694967 1.0495119 4.684412
## X99 2.873869 0.7941114 6.413012
## X100 2.722435 0.8670202 8.433621
## X103 2.876049 0.7671379 8.097921
## X104 2.705439 0.7941114 7.745463
## X105 2.579644 0.8439372 7.745463
## X107 2.752296 1.1760805 6.842982
## X108 2.832888 0.8439372 5.949436
## X109 2.646995 0.7671379 6.842982
## X110 2.750740 1.2265482 5.264609
## X111 2.636011 0.9300710 5.786135
## X112 2.893035 1.1421966 5.617858
## X113 2.875131 1.0186444 8.542148
## X114 2.694190 0.9676836 5.786135
## X115 2.845409 1.0186444 5.949436
## X117 2.806678 1.1970974 6.559746
## X118 2.968193 0.8439372 7.745463
## X121 3.000719 0.7083677 8.097921
## X123 2.706159 1.1760805 4.885442
## X124 2.872708 0.7941114 5.264609
## X126 2.715877 0.9300710 6.842982
## X128 2.763185 0.8670202 8.323453
## X129 2.690241 0.6762264 6.559746
## X130 2.901221 1.0782394 9.260790
## X131 2.532501 0.9100066 6.979888
## X132 2.766469 1.1050693 6.559746
## X133 2.786199 0.9492763 8.323453
## X134 2.791263 1.0186444 9.454406
## X135 2.893035 1.1970974 4.684412
## X136 2.905282 0.7083677 7.745463
## X137 2.568127 0.9676836 7.373808
## X139 2.665023 0.7941114 6.842982
## X140 2.860121 0.9676836 6.702998
## X141 2.604783 1.0343625 8.961066
## X143 2.767852 1.2358607 4.684412
## X144 2.911270 0.8439372 5.264609
## X145 2.674201 1.0343625 5.617858
## X146 2.950670 0.9853483 5.444179
## X147 2.858842 0.8196703 4.254800
## X148 2.908918 0.7941114 4.023747
## X149 2.790112 0.9300710 5.786135
## X152 2.984412 0.9300710 7.113891
## X153 2.731131 0.9853483 7.982407
## X154 2.579644 0.7671379 6.413012
## X155 2.691833 1.1421966 5.078583
## X156 2.926626 0.8670202 6.559746
## X157 2.903482 1.1302068 6.413012
## X158 2.557619 0.9100066 6.702998
## X159 2.845892 0.9492763 7.623847
## X160 2.769220 0.9676836 5.786135
## X161 2.843211 1.0186444 10.528568
## X162 2.875866 0.7671379 6.559746
## X163 2.678590 0.7671379 5.786135
## X165 2.788951 0.8196703 6.413012
## X166 2.662160 0.9100066 7.373808
## X167 2.843948 0.7941114 9.062271
## X168 2.681164 0.9853483 8.097921
## X169 2.824491 1.0641271 4.023747
## X170 2.881391 1.0495119 5.444179
## X171 2.823909 0.9100066 9.549474
## X172 2.694967 1.2265482 6.262563
## X174 2.886307 0.9676836 8.649098
## X175 2.786199 0.8890150 6.979888
## X176 2.823031 1.0023201 6.262563
## X177 2.760303 1.0343625 6.108135
## X178 2.704715 0.9300710 6.108135
## X179 2.891000 1.0782394 7.982407
## X180 3.008161 0.9853483 7.245150
## X181 2.596327 0.7083677 6.979888
## X182 2.773241 1.1178380 5.444179
## X183 2.823325 1.3102163 4.474569
## X184 2.654255 0.8670202 8.542148
## X185 2.771914 0.8670202 8.323453
## X186 2.701785 0.9853483 8.323453
## X189 2.777140 0.7671379 5.078583
## X190 2.839703 0.8670202 6.108135
## X191 2.715205 0.7083677 6.702998
## X192 2.845409 0.7083677 8.097921
## X193 2.852896 0.7083677 9.260790
## X194 2.771023 0.9300710 5.786135
## X195 2.708297 0.7083677 7.745463
## X197 2.815134 0.9676836 8.097921
## X198 2.743782 0.7671379 3.779716
## X200 2.735867 1.0023201 6.559746
## X201 2.785402 0.9300710 7.745463
## X202 2.712482 1.1050693 4.023747
## X205 2.557619 0.7671379 6.979888
## X208 2.824491 1.0023201 8.649098
## X210 2.919789 0.9492763 9.454406
## X212 2.700297 0.9492763 6.262563
## X213 2.754850 1.0186444 8.323453
## X214 2.874020 0.9100066 5.949436
## X215 2.656269 1.1651160 6.108135
## X216 2.752811 0.9676836 7.245150
## X218 2.890046 0.9300710 6.413012
## X219 2.687819 0.8670202 6.842982
## X220 2.594876 0.7671379 7.623847
## X223 2.710405 1.1760805 5.949436
## X224 2.735867 1.2169911 5.786135
## X225 2.744330 1.0343625 6.108135
## X226 2.768310 0.5660797 5.078583
## X227 2.763659 1.2537933 5.264609
## X228 2.653238 1.1970974 5.617858
## X229 2.665023 0.8196703 6.559746
## X230 2.963228 0.9676836 6.413012
## X231 2.767393 1.0782394 10.695079
## X232 2.697275 1.0186444 7.623847
## X233 2.771023 0.7941114 5.444179
## X234 2.715877 1.1050693 7.500000
## X236 2.767393 0.9853483 7.745463
## X237 2.763185 0.9300710 6.262563
## X239 2.875683 1.1302068 4.684412
## X240 2.791263 1.1302068 5.444179
## X241 2.724975 0.9676836 7.245150
## X242 2.654255 0.9300710 6.413012
## X243 2.665966 1.0495119 6.262563
## X244 2.783386 0.9492763 8.211578
## X245 2.692623 0.7941114 5.617858
## X246 2.883623 1.2358607 4.885442
## X247 2.792403 0.6762264 5.444179
## X249 2.665023 1.0186444 7.500000
## X250 2.870614 0.9100066 6.108135
## X251 2.737602 0.7671379 6.559746
## X253 3.008576 1.0186444 7.113891
## X254 3.011822 1.1421966 7.500000
## X255 2.769220 1.0023201 8.961066
## X256 2.393337 0.6762264 7.373808
## X257 2.584357 1.0186444 6.702998
## X258 2.906102 0.7941114 7.500000
## X260 2.822737 0.8196703 7.500000
## X261 2.695741 1.0918780 8.649098
## X262 2.530419 0.7941114 6.108135
## X263 2.807684 0.8196703 10.444102
## X264 2.763185 0.9300710 5.264609
## X265 2.611519 0.8439372 5.949436
## X267 3.009810 0.9853483 8.097921
## X268 2.906780 0.8670202 7.500000
## X269 2.524026 1.0641271 5.078583
## X270 2.735284 0.5237995 8.097921
## X271 2.910232 0.8670202 8.157135
## X272 2.735284 0.9300710 6.413012
## X273 2.842468 0.8439372 6.702998
## X274 2.785402 0.9100066 8.433621
## X275 2.825933 1.1538270 6.262563
## X277 2.943646 1.0343625 7.500000
## X278 2.937016 1.1421966 4.474569
## X279 2.788951 0.9853483 7.245150
## X281 2.875131 0.8890150 10.185606
## X282 2.900935 0.9676836 3.067147
## X283 2.953166 0.6762264 6.559746
## X287 2.666903 0.7671379 7.373808
## X289 2.786596 1.2265482 4.254800
## X290 2.701785 0.7671379 5.786135
## X291 2.839193 1.0343625 7.982407
## X292 2.848747 1.1538270 7.113891
## X294 2.627850 1.3026978 4.254800
## X297 2.751261 0.9676836 6.413012
## X298 2.657266 0.9853483 5.444179
## X299 2.788951 0.9853483 8.542148
## X301 2.620513 0.7083677 7.864950
## X302 2.584357 1.2537933 3.678009
## X303 2.760303 0.9853483 6.413012
## X304 2.700297 0.8670202 9.643429
## X305 2.905965 0.7941114 8.858503
## X306 2.732330 1.1421966 5.949436
## X307 2.945455 1.2071782 5.617858
## X308 2.781750 1.2071782 6.262563
## X311 2.854245 0.9300710 8.211578
## X312 2.810980 0.7671379 10.185606
## X313 2.800812 0.9100066 7.864950
## X314 2.568127 0.7941114 7.623847
## X315 2.846133 1.1651160 4.474569
## X316 2.603403 0.6762264 7.500000
## X317 2.766469 0.8439372 5.078583
## X320 2.666903 0.9853483 7.500000
## X321 2.738746 1.0186444 4.474569
## X322 2.863047 0.9300710 9.549474
## X323 2.913692 0.8439372 5.444179
## X324 2.847566 1.1421966 5.786135
## X325 3.006900 0.7941114 5.444179
## X326 2.864685 0.6419718 5.444179
## X327 2.897137 1.0186444 7.113891
## X329 2.749166 0.7941114 8.323453
## X330 2.713850 1.0186444 5.949436
## X331 2.678590 0.8670202 5.786135
## X332 2.748106 0.9676836 5.786135
## X333 2.862841 0.5660797 6.108135
## HCC_4 Hepatocyte_Growth_Factor_HGF I_309 ICAM_1 IGF_BP_2
## X1 -3.036554 0.58778666 3.433987 -0.19077873 5.609472
## X2 -4.074542 0.53062825 3.135494 -0.46201723 5.347108
## X3 -3.649659 0.09531018 2.397895 -0.46201723 5.181784
## X5 -3.146555 0.53062825 3.761200 0.09715030 5.420535
## X6 -3.079114 0.09531018 2.708050 -0.93510686 5.056246
## X7 -3.506558 0.40546511 2.995732 -0.63604036 5.438079
## X8 -3.079114 0.18232156 2.890372 -1.29887557 5.365976
## X9 -4.135167 -0.16251893 3.295837 -1.06412706 5.273000
## X11 -3.540459 0.40546511 3.091042 -0.56067607 5.505332
## X12 -2.918771 0.09531018 3.258097 -0.73298708 5.081404
## X14 -3.816713 0.53062825 2.944439 -0.17955518 5.209486
## X16 -3.575551 0.18232156 3.332205 -1.06412706 5.375278
## X17 -3.816713 0.18232156 2.639057 -0.66750355 5.455321
## X18 -4.135167 -0.24846136 2.708050 -0.93510686 5.087596
## X19 -3.057608 -0.19845094 2.944439 -0.93510686 5.379897
## X20 -3.772261 0.09531018 2.708050 -0.17955518 5.513429
## X21 -3.437118 0.09531018 2.944439 -1.11122739 5.361292
## X22 -3.863233 0.74193734 3.465736 -0.48946700 5.141664
## X23 -3.816713 -0.07257069 2.484907 -1.16085462 4.955827
## X24 -3.411248 0.69314718 2.708050 -0.38282097 5.472271
## X25 -3.611918 0.09531018 2.890372 -0.56067607 5.187386
## X26 -3.473768 0.33647224 3.258097 -0.66750355 5.257495
## X28 -3.688879 0.33647224 2.197225 -0.80232932 5.438079
## X29 -3.816713 -0.10536052 2.302585 -0.85726607 4.997212
## X30 -4.074542 -0.24846136 2.639057 -0.63604036 5.147494
## X31 -3.194183 0.09531018 3.044522 -0.25991011 5.513429
## X34 -3.381395 0.33647224 3.583519 -1.16085462 5.273000
## X35 -3.218876 -0.19845094 3.091042 -0.48946700 4.836282
## X36 -3.688879 0.18232156 2.944439 -0.30783617 5.598422
## X37 -3.411248 0.47000363 3.135494 -0.66750355 5.351858
## X38 -3.611918 0.18232156 2.639057 -0.60535429 5.141664
## X39 -4.017384 0.64185389 2.833213 -0.91510681 5.252273
## X40 -4.017384 0.09531018 3.178054 -0.59028711 5.407172
## X41 -3.270169 0.00000000 2.708050 -0.39571116 5.327876
## X42 -3.863233 0.09531018 2.890372 -0.73298708 5.420535
## X43 -3.352407 0.18232156 3.258097 -0.69979867 5.198497
## X44 -3.649659 0.26236426 2.708050 0.00000000 5.420535
## X45 -3.170086 0.33647224 3.555348 -0.23651381 5.645447
## X46 -3.575551 0.47000363 3.135494 -0.59028711 5.267858
## X47 -3.506558 -0.31471074 2.995732 -0.87620360 5.351858
## X48 -3.381395 0.64185389 3.761200 -0.42185317 5.700444
## X50 -3.863233 0.26236426 3.332205 -0.48946700 5.262690
## X51 -3.963316 -0.01005034 3.044522 -1.04142304 5.398163
## X53 -3.649659 0.47000363 2.995732 -0.35738780 5.278115
## X55 -3.863233 0.33647224 2.564949 -0.25991011 5.030438
## X56 -3.816713 -0.17435339 2.944439 -0.48946700 5.262690
## X57 -3.123566 0.40546511 3.295837 -0.48946700 5.641907
## X59 -3.611918 0.18232156 2.944439 -0.39571116 5.616771
## X60 -3.575551 -0.17435339 3.218876 -0.63604036 5.438079
## X61 -4.074542 -0.07257069 2.708050 -0.76713789 5.192957
## X62 -3.772261 -0.32850407 2.484907 -0.76713789 5.123964
## X63 -3.442019 0.09531018 3.091042 -0.60535429 5.283204
## X64 -3.146555 0.26236426 3.178054 -0.30783617 5.370638
## X65 -4.135167 0.18232156 2.833213 -0.73298708 5.164786
## X67 -3.688879 0.58778666 3.465736 -0.48946700 5.356586
## X68 -3.218876 0.33647224 2.944439 -0.39571116 5.429346
## X69 -3.863233 -0.43078292 2.564949 -0.93510686 5.141664
## X70 -4.074542 0.18232156 3.465736 -0.56067607 5.594711
## X71 -3.296837 0.09531018 3.258097 -0.10317121 5.068904
## X72 -3.270169 0.87546874 3.332205 -0.16503132 5.780744
## X73 -3.352407 0.47000363 3.433987 0.00000000 5.509388
## X74 -3.442019 -0.19845094 2.639057 -0.69979867 5.241747
## X75 -3.729701 0.40546511 3.663562 -0.91510681 5.337538
## X76 -3.352407 0.69314718 3.433987 -0.30783617 5.549076
## X77 -3.270169 0.58778666 2.944439 -0.48946700 5.129899
## X78 -2.733368 0.64185389 3.465736 -0.16841247 5.624018
## X80 -3.473768 0.09531018 3.044522 -0.53172814 5.204007
## X81 -3.729701 -0.02020271 3.258097 -0.73298708 5.111988
## X82 -3.863233 0.33647224 2.708050 -1.11122739 5.521461
## X83 -3.146555 0.26236426 2.564949 -0.78459824 4.905275
## X84 -3.540459 0.26236426 2.772589 -0.73298708 5.043425
## X85 -3.296837 0.47000363 3.044522 -0.39571116 5.370638
## X86 -2.207275 0.47000363 2.995732 -0.15734908 5.590987
## X88 -3.146555 0.40546511 3.367296 -0.25991011 5.501258
## X90 -2.995732 -0.63487827 1.757858 -1.16085462 4.634729
## X93 -3.270169 0.09531018 2.944439 -0.48946700 5.497168
## X94 -3.123566 0.64185389 4.143135 0.09715030 5.948035
## X95 -3.912023 0.26236426 2.890372 -0.57539617 5.327876
## X96 -3.270169 0.47000363 3.178054 -1.32948627 5.509388
## X97 -3.575551 0.33647224 2.708050 -0.47567232 5.356586
## X98 -4.074542 -0.37106368 2.484907 -1.04142304 5.075174
## X99 -3.816713 0.47000363 3.258097 -0.17955518 5.370638
## X100 -3.729701 0.26236426 3.258097 -0.48946700 5.187386
## X103 -3.649659 0.64185389 3.784190 -0.07152751 5.641907
## X104 -4.509860 0.09531018 2.772589 -0.71627709 5.159055
## X105 -4.074542 0.47000363 2.995732 -0.59028711 5.231109
## X107 -3.772261 0.26236426 3.044522 -0.42185317 5.214936
## X108 -3.123566 0.18232156 2.890372 -0.06111597 5.579730
## X109 -3.912023 -0.04082199 2.292535 -0.91510681 5.003946
## X110 -4.017384 -0.16251893 2.890372 -0.76713789 5.062595
## X111 -3.649659 0.18232156 2.890372 -0.23651381 5.347108
## X112 -2.813411 0.53062825 3.433987 -0.19077873 5.609472
## X113 -3.079114 0.47000363 3.091042 -0.16841247 5.398163
## X114 -3.381395 -0.32850407 2.397895 -0.66750355 5.081404
## X115 -3.381395 0.09531018 3.465736 -1.36134745 5.420535
## X117 -4.135167 0.40546511 3.401197 -0.57539617 5.293305
## X118 -3.352407 0.64185389 3.610918 -0.43511112 5.616771
## X121 -3.575551 0.40546511 3.332205 -0.12462010 5.420535
## X123 -3.611918 -0.44628710 2.833213 -0.93510686 5.407172
## X124 -2.995732 -0.04082199 2.890372 -0.17955518 5.402677
## X126 -4.017384 0.00000000 2.944439 -0.60535429 5.135798
## X128 -3.324236 0.53062825 3.178054 -0.39571116 5.303305
## X129 -3.575551 -0.18632958 2.708050 -0.87620360 5.257495
## X130 -3.352407 0.26236426 2.995732 -0.66750355 5.433722
## X131 -3.688879 0.09531018 2.564949 -0.65167154 4.969813
## X132 -3.575551 0.09531018 2.639057 -0.76713789 5.375278
## X133 -3.170086 0.26236426 2.484907 -0.80232932 5.620401
## X134 -3.015935 0.87546874 3.332205 -0.34483937 5.521461
## X135 -3.649659 0.00000000 3.258097 -0.25991011 5.455321
## X136 -4.135167 -0.15082289 2.564949 -0.48946700 5.451038
## X137 -3.057608 -0.18632958 2.397895 -0.91510681 5.159055
## X139 -3.473768 0.09531018 2.639057 -1.11122739 5.153292
## X140 -3.863233 0.26236426 2.833213 -0.47567232 5.433722
## X141 -3.244194 0.33647224 3.044522 -0.30783617 5.293305
## X143 -3.462900 -0.11653382 2.944439 -0.93510686 5.313206
## X144 -3.442019 -0.21072103 2.708050 -0.30783617 5.356586
## X145 -3.963316 0.09531018 2.639057 -0.85726607 5.164786
## X146 -3.057608 0.40546511 2.564949 -0.05077067 5.645447
## X147 -4.017384 0.33647224 3.465736 -0.38282097 5.342334
## X148 -2.748872 0.33647224 3.135494 -0.11385950 5.468060
## X149 -3.772261 0.18232156 2.833213 -0.46201723 5.081404
## X152 -3.473768 0.78845736 3.761200 -0.16841247 5.758902
## X153 -2.120264 0.47000363 3.295837 -1.21340622 5.609472
## X154 -3.611918 0.33647224 1.808289 -0.73298708 5.181784
## X155 -3.772261 0.09531018 2.890372 -0.63604036 5.062595
## X156 -3.411248 0.64185389 3.258097 -0.14636351 5.332719
## X157 -3.611918 0.18232156 2.995732 -0.53172814 5.613128
## X158 -3.912023 0.18232156 2.833213 -0.59028711 5.198497
## X159 -3.442019 0.09531018 2.995732 -0.87620360 5.093750
## X160 -3.575551 0.53062825 3.091042 -0.25991011 5.389072
## X161 -3.473768 0.18232156 3.433987 -0.39571116 5.135798
## X162 -3.611918 0.64185389 2.944439 -0.59028711 5.342334
## X163 -3.649659 0.26236426 2.564949 -1.16085462 5.030438
## X165 -3.506558 0.18232156 3.218876 -0.73298708 5.455321
## X166 -3.912023 0.09531018 2.995732 -0.87620360 5.379897
## X167 -3.963316 0.58778666 2.944439 -0.17955518 5.342334
## X168 -3.575551 0.33647224 2.639057 -0.73298708 5.209486
## X169 -3.218876 0.09531018 2.772589 -0.66750355 5.153292
## X170 -3.270169 0.09531018 2.995732 -0.66750355 5.187386
## X171 -3.296837 0.09531018 3.258097 -0.15734908 5.117994
## X172 -3.772261 0.09531018 2.890372 -0.93510686 5.075174
## X174 -3.575551 -0.01005034 2.890372 -0.48946700 5.298317
## X175 -2.975930 0.40546511 3.178054 -0.16841247 5.476464
## X176 -3.079114 -0.06187540 2.944439 -0.39571116 5.056246
## X177 -3.540459 0.09531018 2.890372 -0.59028711 5.468060
## X178 -4.509860 -0.21072103 3.178054 -0.87620360 4.983607
## X179 -2.551046 0.26236426 3.332205 -0.30783617 5.517453
## X180 -3.649659 0.40546511 3.126272 0.18913439 5.655992
## X181 -3.506558 -0.19845094 2.484907 -0.85726607 5.093750
## X182 -3.611918 0.18232156 3.218876 -0.85726607 5.424950
## X183 -3.575551 -0.38566248 2.564949 -0.34483937 5.365976
## X184 -3.244194 0.33647224 3.401197 -0.47567232 5.648974
## X185 -3.270169 0.47000363 3.044522 -0.48946700 5.204007
## X186 -3.324236 -0.07257069 2.014903 -0.93510686 5.198497
## X189 -3.688879 0.18232156 2.944439 -0.66750355 5.164786
## X190 -3.575551 0.09531018 2.944439 -0.48946700 5.323010
## X191 -3.506558 0.26236426 2.639057 -0.71627709 4.983607
## X192 -3.381395 0.64185389 2.995732 -0.17955518 5.327876
## X193 -3.611918 0.40546511 3.178054 -0.60535429 5.645447
## X194 -3.270169 -0.05129329 2.564949 -0.59028711 5.209486
## X195 -3.649659 0.83290912 3.433987 -0.17955518 5.303305
## X197 -3.057608 0.26236426 3.091042 -0.56067607 5.560682
## X198 -3.611918 -0.02020271 2.397895 -0.71627709 5.159055
## X200 -3.912023 0.09531018 2.564949 -1.04142304 5.323010
## X201 -3.218876 0.00000000 3.178054 -0.71627709 5.247024
## X202 -3.912023 0.00000000 2.564949 -1.13570251 5.105945
## X205 -3.324236 0.40546511 3.332205 -0.35738780 5.488938
## X208 -3.611918 0.40546511 2.484907 -0.59028711 5.192957
## X210 -3.352407 0.26236426 3.295837 -0.38282097 5.613128
## X212 -4.074542 0.09531018 2.564949 -0.54612169 5.187386
## X213 -3.057608 0.18232156 3.178054 -0.43511112 5.407172
## X214 -3.772261 0.53062825 3.044522 -0.30783617 5.293305
## X215 -3.649659 0.00000000 3.258097 -0.93510686 5.288267
## X216 -3.816713 -0.05129329 3.178054 -1.04142304 5.105945
## X218 -2.847312 0.09531018 2.772589 -0.85726607 5.327876
## X219 -4.199705 -0.11653382 2.772589 -0.87620360 5.327876
## X220 -4.017384 0.09531018 3.044522 -0.97630091 5.030438
## X223 -3.575551 0.09531018 3.258097 -0.57539617 5.303305
## X224 -3.688879 0.18232156 2.995732 -0.63604036 5.147494
## X225 -3.863233 0.40546511 3.433987 -0.22495053 5.472271
## X226 -3.611918 0.74193734 3.259500 -0.32006598 5.620401
## X227 -2.864704 0.00000000 3.044522 -0.06111597 5.293305
## X228 -4.074542 -0.44628710 3.332205 -0.85726607 5.023881
## X229 -3.411248 -0.12783337 2.197225 -0.66750355 5.192957
## X230 -2.882404 0.09531018 3.218876 -0.51749076 5.342334
## X231 -3.442019 0.09531018 2.833213 -0.66750355 5.187386
## X232 -3.101093 0.09531018 2.302585 -0.59028711 5.389072
## X233 -3.352407 -0.11653382 2.833213 -1.04142304 5.187386
## X234 -3.244194 -0.15082289 2.890372 -0.80232932 5.407172
## X236 -3.688879 0.26236426 2.772589 -0.56067607 5.081404
## X237 -3.442019 0.33647224 2.995732 -0.39571116 5.192957
## X239 -4.199705 -0.24846136 2.708050 -0.76713789 5.141664
## X240 -3.411248 0.00000000 2.772589 -1.26939244 5.093750
## X241 -3.324236 0.26236426 3.178054 -1.21340622 5.129899
## X242 -2.956512 -0.13926207 2.772589 -0.54602499 5.288267
## X243 -3.381395 0.00000000 2.564949 -1.06412706 5.252273
## X244 -3.101093 0.26236426 2.944439 -0.48946700 5.365976
## X245 -2.956512 -0.23572233 2.140066 -1.53320769 4.663439
## X246 -2.937463 0.18232156 2.833213 -0.42185317 5.583496
## X247 -3.411248 -0.08338161 2.708050 -1.29887557 5.093750
## X249 -3.688879 0.87546874 3.555348 -0.51749076 5.609472
## X250 -3.146555 0.26236426 2.833213 -1.16085462 5.303305
## X251 -3.816713 0.00000000 2.995732 -0.60535429 5.257495
## X253 -3.270169 0.00000000 3.044522 -0.03027441 5.342334
## X254 -3.218876 0.40546511 3.806662 0.51708817 5.598422
## X255 -3.506558 0.40546511 3.295837 -0.09255394 5.472271
## X256 -3.688879 0.09531018 2.639057 -0.87620360 5.176150
## X257 -3.575551 -0.17435339 2.833213 -0.73298708 5.093750
## X258 -2.937463 0.64185389 3.496508 -0.04049051 5.351858
## X260 -3.101093 0.33647224 3.295837 -0.71627709 5.273000
## X261 -3.411248 -0.26136476 3.044522 -1.06412706 4.682131
## X262 -4.017384 -0.03045921 2.484907 -1.04142304 4.976734
## X263 -3.506558 0.33647224 2.564949 -0.48946700 5.407172
## X264 -2.813411 0.40546511 3.433987 -0.09255394 5.327876
## X265 -3.540459 0.18232156 2.772589 -0.93510686 5.252273
## X267 -3.411248 0.47000363 2.995732 -0.47567232 5.624018
## X268 -3.079114 0.18232156 2.944439 -0.39571116 5.283204
## X269 -3.863233 -0.12783337 2.564949 -0.85726607 5.262690
## X270 -3.649659 0.09531018 3.526361 -0.39571116 5.220356
## X271 -2.918771 0.53062825 3.295837 -0.17955518 5.438079
## X272 -3.729701 0.26236426 2.708050 -0.59028711 5.192957
## X273 -3.057608 0.33647224 2.708050 -0.48946700 5.594711
## X274 -3.540459 0.09531018 2.944439 -0.71627709 5.159055
## X275 -3.506558 0.09531018 3.044522 -0.93510686 5.342334
## X277 -3.688879 0.33647224 2.890372 -0.30783617 5.075174
## X278 -3.506558 -0.11653382 2.995732 -0.19077873 5.579730
## X279 -3.540459 0.09531018 2.772589 -0.93510686 5.087596
## X281 -3.036554 0.53062825 3.135494 -0.80232932 5.517453
## X282 -3.352935 0.26236426 2.397895 -0.25991011 5.288267
## X283 -3.194183 0.64185389 3.332205 -0.39571116 5.777652
## X287 -3.729701 0.58778666 3.044522 -1.18673610 5.135798
## X289 -3.863233 -0.31471074 2.484907 -0.85726607 5.087596
## X290 -4.017384 0.09531018 3.044522 -0.91510681 5.327876
## X291 -3.244194 0.58778666 3.332205 -0.38282097 5.365976
## X292 -3.381395 0.64185389 3.044522 -0.34483937 5.525453
## X294 -3.688879 -0.41551544 2.833213 -1.26939244 5.442418
## X297 -3.442019 0.00000000 2.995732 -0.60535429 5.323010
## X298 -3.411248 -0.24846136 2.041220 -0.65167154 5.521461
## X299 -3.575551 0.18232156 2.995732 -0.43511112 5.662960
## X301 -3.442019 -0.23572233 2.708050 -1.06412706 5.257495
## X302 -3.649659 -0.31471074 2.708050 -1.13570251 4.962845
## X303 -3.540459 0.47000363 2.772589 -0.93510686 5.384495
## X304 -3.912023 0.78845736 3.784190 -0.30783617 5.587249
## X305 -3.688879 0.47000363 2.484907 -0.59028711 5.693732
## X306 -3.194183 0.47000363 3.555348 -0.25991011 5.480639
## X307 -2.975930 0.33647224 3.135494 0.27662577 5.541264
## X308 -3.079114 0.09531018 2.833213 -0.53172814 5.181784
## X311 -3.688879 -0.26136476 3.091042 -0.48946700 5.225747
## X312 -3.270169 0.26236426 3.135494 -0.73298708 5.214936
## X313 -3.611918 0.26236426 2.564949 -0.71627709 5.638355
## X314 -3.816713 0.26236426 2.564949 -1.04142304 5.236442
## X315 -3.411248 -0.04082199 2.944439 -0.53172814 5.262690
## X316 -4.074542 0.47000363 3.044522 -0.46201723 5.332719
## X317 -4.074542 0.33647224 3.218876 -0.59028711 5.505332
## X320 -3.057608 0.18232156 2.397895 -0.17955518 5.323010
## X321 -3.729701 -0.37106368 2.772589 -0.46201723 4.976734
## X322 -3.057608 0.47000363 2.708050 -0.48946700 5.402677
## X323 -3.079114 0.40546511 3.332205 -0.43511112 5.424950
## X324 -3.688879 0.18232156 2.639057 -0.76713789 5.135798
## X325 -3.146555 0.33647224 2.186051 -0.59028711 5.472271
## X326 -3.473768 0.18232156 2.995732 -0.48946700 5.389072
## X327 -3.324236 0.09531018 3.091042 -0.66750355 5.609472
## X329 -3.912023 0.40546511 3.044522 -0.73298708 5.278115
## X330 -3.244194 0.09531018 2.944439 -0.38282097 5.209486
## X331 -3.101093 0.00000000 2.240710 -0.85726607 5.087596
## X332 -3.296837 -0.01005034 2.028148 -0.59028711 5.411646
## X333 -3.649659 0.64185389 3.295837 -0.32006598 5.552960
## IL_11 IL_13 IL_16 IL_17E IL_1alpha IL_3 IL_4
## X1 5.121987 1.282549 4.192081 5.731246 -6.571283 -3.244194 2.4849066
## X2 4.936704 1.269463 2.876338 6.705891 -8.047190 -3.912023 2.3978953
## X3 4.665910 1.274133 2.616102 4.149327 -8.180721 -4.645992 1.8245493
## X5 7.070709 1.309980 4.736472 4.204987 -6.943657 -2.995732 2.7080502
## X6 6.103215 1.282549 2.671032 3.637051 -8.180721 -3.863233 1.2089603
## X7 2.031412 1.286356 3.476091 6.705891 -6.907755 -3.296837 1.8718022
## X8 5.180840 1.293295 3.593860 4.037285 -7.418581 -2.956512 2.3978953
## X9 2.860031 1.282549 2.420557 5.170380 -7.469874 -4.422849 1.8082888
## X11 6.919778 1.274133 2.154845 4.749337 -7.849364 -4.509860 1.5686159
## X12 3.218759 1.286356 3.593860 3.867347 -8.047190 -3.575551 1.9169226
## X14 4.665910 1.269463 2.924466 5.427755 -7.264430 -4.074542 1.5260563
## X16 4.360856 1.278484 2.776394 5.170380 -7.662778 -4.017384 1.5475625
## X17 5.121987 1.286356 2.671032 5.325310 -7.047017 -3.912023 1.2237754
## X18 3.076971 1.274133 2.270306 4.749337 -7.182192 -4.933674 1.0296194
## X19 5.180840 1.293295 2.924466 7.174674 -7.542634 -3.912023 2.3978953
## X20 2.178537 1.274133 2.741908 4.149327 -7.264430 -3.816713 1.8245493
## X21 6.414275 1.274133 2.292540 5.580204 -7.236259 -5.035953 1.8082888
## X22 4.805045 1.293295 3.351388 8.081107 -7.542634 -3.772261 1.2089603
## X23 4.518270 1.282549 2.461332 5.427755 -7.264430 -4.919881 1.0296194
## X24 4.360856 1.289931 3.476091 5.325310 -6.980326 -3.442019 2.3978953
## X25 4.936704 1.274133 1.662234 3.040333 -7.706263 -4.074542 1.5686159
## X26 4.360856 1.282549 2.420557 4.204987 -7.523941 -4.509860 1.8082888
## X28 3.593860 1.269463 3.076971 4.749337 -7.278819 -3.816713 1.2237754
## X29 5.752800 1.302302 2.924466 5.325310 -7.487574 -4.605170 3.0445224
## X30 3.593860 1.282549 2.520871 4.749337 -7.469874 -3.411248 1.9740810
## X31 5.121987 1.278484 3.705506 5.731246 -7.143478 -3.324236 2.5649494
## X34 2.031412 1.274133 2.081821 5.170380 -7.264430 -4.422849 2.0149030
## X35 3.476091 1.282549 2.420557 5.170380 -7.849364 -4.509860 1.6863990
## X36 2.031412 1.299465 3.705506 3.040333 -7.106206 -3.057608 2.1860513
## X37 5.238363 1.278484 2.671032 4.204987 -7.581100 -3.912023 1.8082888
## X38 4.360856 1.286356 2.578583 3.578777 -7.250246 -3.729701 1.5475625
## X39 7.011244 1.293295 3.913012 4.802628 -7.435388 -3.244194 1.8245493
## X40 5.705637 1.264435 2.292540 6.705891 -7.264430 -3.912023 1.2089603
## X41 3.351388 1.286356 3.811702 3.578777 -7.600902 -3.688879 1.5475625
## X42 6.061784 1.269463 2.578583 4.149327 -7.264430 -4.779524 0.5877867
## X43 3.913012 1.274133 2.597435 5.731246 -7.706263 -4.342806 1.2089603
## X44 5.558929 1.278484 3.476091 6.705891 -7.264430 -4.509860 1.5260563
## X45 3.913012 1.286356 3.593860 4.749337 -7.469874 -3.772261 2.1747517
## X46 4.518270 1.278484 3.705506 4.802628 -8.294050 -4.135167 1.0296194
## X47 4.102821 1.264435 1.898648 5.630705 -7.264430 -4.509860 1.1939225
## X48 6.019723 1.286356 4.009916 5.731246 -6.907755 -3.218876 1.6094379
## X50 7.730538 1.289931 3.476091 6.705891 -7.849364 -4.422849 1.8082888
## X51 5.349675 1.289931 2.292540 5.731246 -7.264430 -4.017384 1.2089603
## X53 5.799150 1.289931 3.476091 5.427755 -8.047190 -3.688879 1.5260563
## X55 5.558929 1.274133 2.876338 4.802628 -7.264430 -4.268698 1.0296194
## X56 2.031412 1.289931 2.270306 6.225224 -7.402052 -3.816713 1.8082888
## X57 8.004073 1.304993 3.913012 7.174674 -7.323271 -3.772261 1.8245493
## X59 2.031412 1.293295 2.827004 4.149327 -8.047190 -4.268698 1.1939225
## X60 3.913012 1.278484 2.876338 5.325310 -7.706263 -4.017384 1.4586150
## X61 6.414275 1.269463 2.441056 4.204987 -7.706263 -4.017384 1.0296194
## X62 2.843587 1.259002 1.386542 6.225224 -7.641724 -4.268698 1.4586150
## X63 3.218759 1.269463 2.776394 4.149327 -8.047190 -4.268698 1.5475625
## X64 2.860031 1.286356 3.705506 5.630705 -7.662778 -3.863233 2.0149030
## X65 4.518270 1.269463 2.741908 4.802628 -8.294050 -4.074542 2.0412203
## X67 3.705506 1.269463 2.081821 2.407182 -7.264430 -4.815891 1.5475625
## X68 5.608737 1.278484 3.593860 6.705891 -7.542634 -3.912023 1.4816045
## X69 5.238363 1.286356 1.186565 4.204987 -7.469874 -4.635629 1.6094379
## X70 3.705506 1.282549 2.671032 5.325310 -7.706263 -3.772261 1.5686159
## X71 4.278004 1.278484 2.616102 4.802628 -8.294050 -4.199705 1.0296194
## X72 5.180840 1.304993 4.593226 3.461346 -6.907755 -2.718101 2.6390573
## X73 4.102821 1.274133 3.476091 5.118391 -8.047190 -4.017384 1.0296194
## X74 3.913012 1.259002 2.081821 6.225224 -7.706263 -4.635629 1.2089603
## X75 3.913012 1.286356 3.913012 1.052263 -7.849364 -3.649659 2.3978953
## X76 2.688997 1.282549 3.218759 5.170380 -7.662778 -4.017384 1.1939225
## X77 6.223931 1.293295 2.860031 6.225224 -7.684284 -4.074542 1.8245493
## X78 3.593860 1.282549 3.811702 5.325310 -7.706263 -3.506558 2.0149030
## X80 5.558929 1.289931 2.924466 6.705891 -7.182192 -3.611918 1.7404662
## X81 4.736472 1.278484 3.218759 4.370576 -7.662778 -3.772261 2.3025851
## X82 5.456396 1.282549 2.420557 4.204987 -7.581100 -4.017384 1.5686159
## X83 6.760790 1.293295 3.476091 8.951879 -7.418581 -3.473768 1.6677068
## X84 2.501234 1.278484 2.081821 6.225224 -7.662778 -4.733004 1.8082888
## X85 3.476091 1.286356 3.705506 6.225224 -7.024289 -3.442019 2.4849066
## X86 4.805045 1.293295 3.913012 3.867347 -6.725434 -2.796881 2.7725887
## X88 5.403583 1.293295 3.218759 5.325310 -7.523941 -4.268698 1.0296194
## X90 4.360856 1.282549 1.813803 5.325310 -8.111728 -5.914504 1.2089603
## X93 5.000000 1.307549 3.351388 7.174674 -7.684284 -4.135167 2.0668628
## X94 3.705506 1.286356 4.360856 5.630705 -6.812445 -3.506558 2.1860513
## X95 5.799150 1.293295 3.076971 4.749337 -7.264430 -3.575551 1.9740810
## X96 6.857203 1.274133 2.876338 4.749337 -7.706263 -4.074542 1.5686159
## X97 5.799150 1.274133 2.671032 4.204987 -8.078938 -4.767689 1.5686159
## X98 5.121987 1.274133 2.178537 5.325310 -7.706263 -4.135167 1.4586150
## X99 3.913012 1.289931 4.102821 4.479850 -7.849364 -3.506558 2.4849066
## X100 5.121987 1.264435 2.924466 5.170380 -7.849364 -4.342806 1.5475625
## X103 4.102821 1.282549 3.476091 6.225224 -7.849364 -3.057608 1.8245493
## X104 5.000000 1.309980 2.860031 6.705891 -7.542634 -3.816713 2.3978953
## X105 5.000000 1.289931 3.351388 4.802628 -7.849364 -3.863233 2.0412203
## X107 4.278004 1.286356 3.076971 6.705891 -7.264430 -3.473768 1.9740810
## X108 2.031412 1.286356 4.009916 4.749337 -6.927958 -3.411248 2.7080502
## X109 5.180840 1.264435 2.106497 6.225224 -7.264430 -4.815891 1.0296194
## X110 4.593226 1.274133 2.081821 4.204987 -7.354042 -4.509860 1.9740810
## X111 5.000000 1.299465 3.476091 5.630705 -7.542634 -3.575551 2.0668628
## X112 4.102821 1.302302 4.440875 4.749337 -6.725434 -2.918771 2.9444390
## X113 4.593226 1.286356 3.351388 3.924249 -7.118476 -3.506558 2.3025851
## X114 5.000000 1.274133 2.292540 2.728930 -7.849364 -4.422849 1.2089603
## X115 1.754800 1.278484 2.924466 4.149327 -7.264430 -3.963316 0.5877867
## X117 4.440875 1.278484 3.476091 5.731246 -7.058578 -3.649659 1.7404662
## X118 7.157766 1.286356 3.476091 5.325310 -7.369791 -3.649659 2.0149030
## X121 2.031412 1.274133 3.076971 2.407182 -7.751725 -4.017384 1.8082888
## X123 5.799150 1.274133 1.564217 4.749337 -7.849364 -4.815891 1.4586150
## X124 6.557896 1.286356 3.076971 7.174674 -7.542634 -3.816713 1.8245493
## X126 3.218759 1.282549 2.924466 6.225224 -7.662778 -4.509860 1.1939225
## X128 6.103215 1.282549 3.218759 4.149327 -7.662778 -4.074542 1.1939225
## X129 2.860031 1.278484 1.898648 4.149327 -7.264430 -4.422849 0.5877867
## X130 5.558929 1.286356 3.218759 4.749337 -7.581100 -4.342806 1.2237754
## X131 4.665910 1.282549 3.351388 2.005028 -8.294050 -3.729701 2.3978953
## X132 3.476091 1.282549 3.218759 5.325310 -7.469874 -4.074542 1.7404662
## X133 4.936704 1.289931 2.420557 2.407182 -7.278819 -3.729701 1.8082888
## X134 5.180840 1.289931 3.705506 4.749337 -6.437752 -4.135167 2.0918641
## X135 2.031412 1.286356 2.876338 6.225224 -7.581100 -3.611918 1.4586150
## X136 5.508162 1.293295 2.908553 4.695848 -7.418581 -4.199705 1.2089603
## X137 5.705637 1.269463 1.898648 5.427755 -7.264430 -4.074542 2.1400662
## X139 7.157766 1.264435 2.154845 4.749337 -8.180721 -4.767689 0.5306283
## X140 5.558929 1.269463 2.540305 4.204987 -7.581100 -3.963316 1.8082888
## X141 3.913012 1.269463 2.876338 4.749337 -7.706263 -4.422849 1.8082888
## X143 3.076971 1.282549 2.924466 3.578777 -7.264430 -3.324236 2.2407097
## X144 5.238363 1.274133 1.459970 2.407182 -5.952244 -4.268698 1.4109870
## X145 3.913012 1.286356 2.741908 4.749337 -7.354042 -3.816713 1.4586150
## X146 2.314505 1.286356 2.924466 3.924249 -7.195437 -3.324236 1.8082888
## X147 4.360856 1.278484 2.876338 4.204987 -7.775256 -4.074542 1.2237754
## X148 4.805045 1.320562 4.192081 4.037285 -6.725434 -2.918771 2.2617631
## X149 5.799150 1.282549 2.876338 2.791992 -7.849364 -3.575551 1.8245493
## X152 4.871752 1.293295 4.102821 3.637051 -6.725434 -2.882404 2.8903718
## X153 2.843587 1.282549 3.351388 2.407182 -7.523941 -3.506558 2.1747517
## X154 5.752800 1.274133 2.741908 4.149327 -7.264430 -4.135167 1.8245493
## X155 5.180840 1.274133 2.597435 6.225224 -7.469874 -4.017384 1.0296194
## X156 3.476091 1.278484 3.913012 5.427755 -7.156217 -3.442019 2.2192035
## X157 2.314505 1.289931 3.076971 5.325310 -7.264430 -3.540459 1.7404662
## X158 5.238363 1.269463 2.741908 3.461346 -7.264430 -4.074542 1.0296194
## X159 3.913012 1.289931 2.270306 6.225224 -7.751725 -4.342806 1.8082888
## X160 6.144037 1.286356 3.593860 7.174674 -7.561682 -3.079114 2.9957323
## X161 3.705506 1.286356 2.706796 4.149327 -7.849364 -3.912023 1.8082888
## X162 8.490785 1.293295 3.076971 6.225224 -7.824046 -4.342806 1.8245493
## X163 7.801137 1.296467 1.423676 7.174674 -8.016418 -4.509860 2.0668628
## X165 5.121987 1.274133 2.292540 3.637051 -7.849364 -4.422849 0.9555114
## X166 3.705506 1.282549 2.081821 3.578777 -7.849364 -4.074542 0.5877867
## X167 5.061733 1.296467 3.476091 5.731246 -7.264430 -3.442019 2.6390573
## X168 3.705506 1.282549 3.593860 2.791992 -7.957577 -3.381395 2.8332133
## X169 6.522658 1.282549 2.540305 5.325310 -7.641724 -3.772261 2.3025851
## X170 4.360856 1.278484 2.292540 4.204987 -7.849364 -4.342806 1.8082888
## X171 5.799150 1.282549 3.351388 5.170380 -7.523941 -4.199705 2.0149030
## X172 5.121987 1.274133 2.597435 3.578777 -7.264430 -3.863233 2.2407097
## X174 3.476091 1.302302 2.706796 5.376615 -7.662778 -3.729701 1.5475625
## X175 6.450860 1.282549 3.218759 3.637051 -7.706263 -3.816713 1.2237754
## X176 2.501234 1.293295 2.924466 6.705891 -7.106206 -4.135167 1.8082888
## X177 4.805045 1.312295 3.705506 5.731246 -7.047017 -3.352407 1.8245493
## X178 3.593860 1.264435 1.898648 4.642159 -8.334872 -4.656463 1.5475625
## X179 2.843587 1.299465 3.476091 3.924249 -7.118476 -3.244194 2.3025851
## X180 4.665910 1.286356 2.420557 2.728930 -7.581100 -3.324236 1.8082888
## X181 5.000000 1.289931 2.652897 7.174674 -8.016418 -4.422849 2.0668628
## X182 3.593860 1.282549 3.218759 5.325310 -7.182192 -3.729701 1.7404662
## X183 2.031412 1.282549 3.218759 6.225224 -7.106206 -3.540459 1.7404662
## X184 4.593226 1.289931 3.476091 3.342694 -7.369791 -3.649659 2.5649494
## X185 2.688997 1.282549 2.706796 5.630705 -7.293418 -4.135167 2.0149030
## X186 3.913012 1.278484 2.357662 5.325310 -8.180721 -4.199705 0.5306283
## X189 5.558929 1.274133 2.154845 4.749337 -7.849364 -4.342806 2.1747517
## X190 5.657628 1.289931 2.908553 6.705891 -7.824046 -3.688879 1.8245493
## X191 5.180840 1.299465 2.924466 4.037285 -7.684284 -3.912023 1.2089603
## X192 4.360856 1.304993 2.924466 5.325310 -7.418581 -3.912023 2.0668628
## X193 2.031412 1.282549 3.218759 1.796259 -7.402052 -3.729701 1.1939225
## X194 7.378459 1.299465 3.076971 3.402176 -7.487574 -3.244194 2.3978953
## X195 4.665910 1.282549 4.192081 5.427755 -7.338538 -3.688879 2.0412203
## X197 5.558929 1.274133 3.218759 3.040333 -7.849364 -4.017384 1.5686159
## X198 6.103215 1.296467 2.597435 4.370576 -7.684284 -3.863233 2.3978953
## X200 6.888655 1.278484 3.076971 5.731246 -7.581100 -3.912023 1.7404662
## X201 6.103215 1.274133 2.292540 5.325310 -7.542634 -4.017384 1.2089603
## X202 6.857203 1.269463 2.081821 4.749337 -7.354042 -3.863233 1.6094379
## X205 6.144037 1.269463 2.616102 3.810182 -7.684284 -3.963316 1.8245493
## X208 4.593226 1.296467 2.924466 3.402176 -7.222466 -3.963316 2.5649494
## X210 6.694714 1.286356 2.420557 4.749337 -7.369791 -4.656463 2.0149030
## X212 2.031412 1.278484 2.578583 1.796259 -7.849364 -4.074542 1.5475625
## X213 5.121987 1.282549 2.671032 5.731246 -7.278819 -4.135167 1.4109870
## X214 4.102821 1.274133 3.351388 4.149327 -8.180721 -3.772261 2.0412203
## X215 5.558929 1.278484 2.597435 6.225224 -7.523941 -4.074542 1.7404662
## X216 5.752800 1.286356 2.520871 8.081107 -7.264430 -4.645992 1.4816045
## X218 5.799150 1.296467 3.218759 4.695848 -7.047017 -3.123566 2.2617631
## X219 5.799150 1.264435 1.386542 1.434661 -7.264430 -4.779524 1.5475625
## X220 3.705506 1.296467 2.578583 3.578777 -7.523941 -4.017384 1.5475625
## X223 4.736472 1.286356 3.218759 4.749337 -6.927958 -3.611918 1.9740810
## X224 2.031412 1.278484 3.076971 4.749337 -7.581100 -3.540459 1.9740810
## X225 4.936704 1.278484 3.076971 5.325310 -7.354042 -3.912023 2.3025851
## X226 4.102821 1.286356 2.924466 7.174674 -7.684284 -3.540459 1.4816045
## X227 3.076971 1.289931 3.351388 4.749337 -6.907755 -3.381395 2.3025851
## X228 3.593860 1.278484 2.876338 2.978813 -7.469874 -3.912023 1.2809338
## X229 6.019723 1.274133 2.292540 4.204987 -7.929407 -4.199705 1.8082888
## X230 3.913012 1.274133 2.924466 4.204987 -7.849364 -3.963316 2.1747517
## X231 6.184269 1.286356 2.671032 5.325310 -7.469874 -4.342806 1.8082888
## X232 6.223931 1.302302 2.860031 5.731246 -7.542634 -3.912023 1.4816045
## X233 4.805045 1.296467 2.106497 4.695848 -8.016418 -3.963316 1.4816045
## X234 4.102821 1.289931 3.076971 5.325310 -7.369791 -3.772261 2.1747517
## X236 6.592710 1.282549 2.154845 4.749337 -7.706263 -4.688552 1.5686159
## X237 4.360856 1.286356 3.076971 5.630705 -7.849364 -3.506558 2.3025851
## X239 3.913012 1.282549 1.564217 4.204987 -7.706263 -3.912023 1.0296194
## X240 4.360856 1.286356 3.076971 3.578777 -7.024289 -4.017384 2.4849066
## X241 3.913012 1.282549 2.924466 5.066223 -7.469874 -4.135167 2.0149030
## X242 2.634588 1.316614 4.936704 2.791992 -6.948287 -2.453408 2.9957323
## X243 2.860031 1.264435 2.578583 1.052263 -7.264430 -4.268698 1.5475625
## X244 3.913012 1.274133 2.578583 5.376615 -7.849364 -4.268698 2.0149030
## X245 6.857203 1.264435 1.693661 7.174674 -7.751725 -5.472671 1.2089603
## X246 4.936704 1.293295 3.593860 2.978813 -6.907755 -3.079114 2.4849066
## X247 6.103215 1.286356 2.292540 7.174674 -8.217089 -4.976234 1.2089603
## X249 3.076971 1.269463 2.776394 4.204987 -7.469874 -3.912023 1.8082888
## X250 3.351388 1.278484 2.616102 4.149327 -8.047190 -3.912023 1.5260563
## X251 2.031412 1.289931 2.357662 3.578777 -7.662778 -4.509860 2.1860513
## X253 3.705506 1.278484 3.076971 2.407182 -7.402052 -3.057608 2.1860513
## X254 2.031412 1.289931 4.440875 5.325310 -7.354042 -3.324236 2.0668628
## X255 6.223931 1.286356 3.218759 4.149327 -7.751725 -3.863233 1.1939225
## X256 4.518270 1.269463 1.898648 3.040333 -7.264430 -4.815891 0.5877867
## X257 7.431248 1.264435 2.501234 5.630705 -8.047190 -4.656463 1.1939225
## X258 4.278004 1.269463 2.461332 4.149327 -7.264430 -4.509860 1.5260563
## X260 5.799150 1.274133 3.351388 3.402176 -7.824046 -3.963316 1.2089603
## X261 4.440875 1.278484 2.270306 4.642159 -8.047190 -5.132803 1.1939225
## X262 6.223931 1.282549 2.520871 6.705891 -7.684284 -4.645992 1.2089603
## X263 5.889532 1.293295 4.009916 4.037285 -7.182192 -3.244194 2.1747517
## X264 2.031412 1.302302 3.811702 5.170380 -6.502290 -3.194183 2.8903718
## X265 5.456396 1.278484 2.597435 5.731246 -8.016418 -3.912023 2.2617631
## X267 3.218759 1.286356 3.476091 3.637051 -7.581100 -3.688879 2.0149030
## X268 6.339666 1.286356 2.860031 8.081107 -7.600902 -3.912023 2.2617631
## X269 4.665910 1.278484 2.081821 4.204987 -7.706263 -4.268698 1.4586150
## X270 4.805045 1.302302 2.652897 5.325310 -7.824046 -3.863233 1.8245493
## X271 7.609273 1.299465 3.076971 5.325310 -7.751725 -3.912023 2.3978953
## X272 4.871752 1.282549 2.520871 8.520578 -7.824046 -4.074542 1.8245493
## X273 7.041123 1.286356 3.076971 6.705891 -8.016418 -4.268698 1.4816045
## X274 5.657628 1.289931 2.597435 4.695848 -7.824046 -4.199705 1.4816045
## X275 6.339666 1.282549 4.192081 3.578777 -6.812445 -3.218876 2.4849066
## X277 3.218759 1.278484 2.776394 3.637051 -7.641724 -4.199705 1.2237754
## X278 2.843587 1.293295 3.218759 4.749337 -7.354042 -3.506558 2.1517622
## X279 4.871752 1.269463 2.671032 3.637051 -7.581100 -4.135167 1.5686159
## X281 5.121987 1.293295 3.218759 4.204987 -7.706263 -3.863233 2.0149030
## X282 3.593860 1.274133 2.924466 4.802628 -7.264430 -3.575551 1.2089603
## X283 6.223931 1.296467 3.351388 5.325310 -7.418581 -3.688879 2.2617631
## X287 4.102821 1.289931 3.076971 5.427755 -8.047190 -3.688879 2.2192035
## X289 3.218759 1.274133 1.952975 5.325310 -7.581100 -4.199705 1.7404662
## X290 4.518270 1.274133 2.106497 2.791992 -7.264430 -4.422849 1.2089603
## X291 6.339666 1.274133 2.876338 5.066223 -7.581100 -4.017384 1.5686159
## X292 6.339666 1.286356 4.009916 4.204987 -7.182192 -3.688879 1.7404662
## X294 2.843587 1.278484 3.076971 3.578777 -7.354042 -3.244194 2.1517622
## X297 4.440875 1.269463 2.420557 4.642159 -7.849364 -4.017384 1.8082888
## X298 4.102821 1.274133 2.671032 3.461346 -8.294050 -3.296837 1.5260563
## X299 6.103215 1.278484 2.671032 5.325310 -7.469874 -4.422849 1.8082888
## X301 3.351388 1.286356 1.898648 4.642159 -8.517193 -4.422849 1.6863990
## X302 3.218759 1.293295 1.842554 5.731246 -7.182192 -4.074542 1.4586150
## X303 5.061733 1.293295 2.597435 3.402176 -7.684284 -4.199705 2.2617631
## X304 5.558929 1.278484 2.420557 5.731246 -7.264430 -4.656463 1.8082888
## X305 6.661108 1.307549 3.593860 6.705891 -7.047017 -3.611918 2.5649494
## X306 6.592710 1.282549 3.593860 5.731246 -7.354042 -3.540459 1.7404662
## X307 6.103215 1.302302 4.360856 5.325310 -6.812445 -3.079114 2.3025851
## X308 4.936704 1.289931 4.009916 4.749337 -6.645391 -2.813411 2.5649494
## X311 2.031412 1.286356 3.593860 3.282892 -7.293418 -3.816713 1.9169226
## X312 7.559432 1.264435 2.827004 5.170380 -7.264430 -4.268698 0.5877867
## X313 8.025855 1.293295 2.860031 1.866476 -7.824046 -3.729701 1.2089603
## X314 7.070709 1.274133 2.201921 5.731246 -7.264430 -4.199705 1.2089603
## X315 3.913012 1.293295 3.476091 4.204987 -6.907755 -3.352407 2.5649494
## X316 4.665910 1.278484 3.076971 6.225224 -7.849364 -3.912023 1.5260563
## X317 3.913012 1.274133 2.924466 3.461346 -7.849364 -3.863233 2.2192035
## X320 7.157766 1.286356 3.218759 4.802628 -8.294050 -3.963316 2.0412203
## X321 4.665910 1.286356 2.924466 2.407182 -7.264430 -3.649659 1.9459101
## X322 3.218759 1.293295 4.192081 7.632751 -6.725434 -3.244194 2.4849066
## X323 7.297547 1.293295 3.218759 6.705891 -7.684284 -4.268698 1.2089603
## X324 5.121987 1.293295 3.076971 4.204987 -7.354042 -4.074542 1.7404662
## X325 4.805045 1.307549 3.476091 5.325310 -7.222466 -3.442019 1.4816045
## X326 5.799150 1.282549 2.860031 5.325310 -7.264430 -4.791500 1.1939225
## X327 4.871752 1.274133 3.218759 3.637051 -7.849364 -3.649659 1.8082888
## X329 2.501234 1.282549 2.924466 5.630705 -7.849364 -4.074542 1.8082888
## X330 2.843587 1.278484 2.876338 4.749337 -8.047190 -4.268698 2.0668628
## X331 6.857203 1.293295 2.005655 5.325310 -8.111728 -3.963316 2.2617631
## X332 5.180840 1.282549 3.593860 6.705891 -7.323271 -3.863233 1.4816045
## X333 6.223931 1.293295 2.860031 4.695848 -7.418581 -3.863233 1.8245493
## IL_5 IL_6 IL_6_Receptor IL_7 IL_8
## X1 1.09861229 0.26936976 0.64279595 4.8050453 1.711325
## X2 0.69314718 0.09622438 0.43115645 3.7055056 1.675557
## X3 -0.24846136 0.18568645 0.09668586 1.0056222 1.691393
## X5 1.16315081 -0.07204658 0.09668586 4.2875620 1.764298
## X6 -0.40047757 0.18568645 -0.51727788 2.7763945 1.708270
## X7 0.83290912 0.09622438 0.43115645 4.0099156 1.698489
## X8 -0.09431068 1.00562217 -0.60969274 3.7055056 1.701858
## X9 -0.15082289 -0.64724718 0.27296583 0.6848724 1.691393
## X11 0.18232156 -1.09654116 0.35404039 2.9244660 1.719944
## X12 0.33647224 -0.39871863 0.09668586 2.9244660 1.675557
## X14 0.09531018 0.49280272 0.00000000 1.0056222 1.760954
## X16 0.26236426 0.42235886 0.18739989 1.2696362 1.705116
## X17 -0.17435339 0.26936976 -0.25138068 2.5785828 1.760954
## X18 -0.75502258 -0.17134851 0.00000000 2.7592279 1.573599
## X19 -0.30110509 -0.37116408 -0.33825519 1.3095734 1.719944
## X20 -0.67334455 -0.07204658 -0.39066087 2.7934108 1.750000
## X21 -0.54472718 -0.09342680 -0.30031103 0.5598079 1.701858
## X22 0.40546511 -1.01892829 0.35404039 3.5938596 1.764298
## X23 -0.13926207 0.62373057 -0.51727788 2.1548454 1.675557
## X24 0.47000363 -0.15990607 0.35404039 3.2187591 1.695003
## X25 -0.71334989 -0.33102365 -0.18121747 1.5642169 1.657003
## X26 -0.17435339 -0.24238905 -0.30031103 1.5642169 1.679744
## X28 0.33647224 -0.09342680 0.27296583 2.1548454 1.671202
## X29 0.09531018 -0.15990607 0.00000000 2.7763945 1.675557
## X30 1.94591015 0.09622438 -0.20419869 3.2187591 1.679744
## X31 0.64185389 0.34805188 -0.12541320 3.2187591 1.772079
## X34 0.00000000 1.53020362 -0.40410820 0.6848724 1.757464
## X35 -0.37106368 -0.64724718 -0.32548281 4.3608562 1.646447
## X36 0.64185389 0.34805188 0.18739989 4.3608562 1.717157
## X37 0.00000000 -0.83723396 0.00000000 1.5642169 1.701858
## X38 -0.37106368 -0.18290044 0.09668586 2.0567968 1.739622
## X39 0.58778666 -1.18086796 -0.05090066 3.9130123 1.657003
## X40 -0.13926207 0.26936976 0.00000000 1.8425543 1.730320
## X41 0.18232156 -0.99435191 0.27296583 3.9130123 1.717157
## X42 0.00000000 -0.18290044 0.18739989 2.1548454 1.711325
## X43 -0.30110509 0.49280272 -0.01003520 3.2187591 1.698489
## X44 0.33647224 -0.24238905 -0.06130466 3.8117017 1.760954
## X45 0.26236426 -0.41274719 0.35404039 3.2187591 1.725279
## X46 0.33647224 -0.07204658 0.00000000 1.8425543 1.727834
## X47 -1.04982212 0.34805188 -0.10371280 2.1548454 1.679744
## X48 0.64185389 -0.07204658 0.35404039 3.9130123 1.701858
## X50 0.58778666 -0.99435191 -0.13639307 2.9244660 1.725279
## X51 0.09531018 -1.45166590 0.50475301 2.3362105 1.661938
## X53 0.09531018 0.26936976 -0.26344327 3.4760910 1.708270
## X55 0.18232156 -0.45589516 0.27296583 2.7934108 1.730320
## X56 -0.15082289 0.00000000 -0.16987200 2.0567968 1.683772
## X57 0.58778666 -0.15990607 0.64279595 3.7055056 1.714286
## X59 -0.15082289 0.18568645 0.27296583 1.6936607 1.735094
## X60 -0.18632958 0.55980793 -0.04057305 2.1548454 1.705116
## X61 0.33647224 -0.17134851 -0.19265903 2.0567968 1.687652
## X62 -0.08338161 0.09622438 -0.12541320 5.1219873 1.695003
## X63 -0.65392647 -0.09342680 0.00000000 2.1548454 1.683772
## X64 0.33647224 1.30957344 0.18739989 1.2696362 1.711325
## X65 -0.67334455 -0.24238905 -0.31283574 1.8425543 1.714286
## X67 0.00000000 -0.27956244 0.27296583 1.2696362 1.675557
## X68 -0.56211892 -1.01892829 -0.08234738 2.7763945 1.695003
## X69 -0.75502258 -0.26704121 -0.13639307 2.1548454 1.683772
## X70 0.58778666 -0.24238905 -0.14746164 2.5785828 1.691393
## X71 -0.13926207 0.09622438 0.35404039 1.0056222 1.714286
## X72 1.19392247 -0.24238905 0.43115645 4.2780037 1.746000
## X73 0.09531018 0.09622438 0.18739989 2.3788658 1.691393
## X74 -1.42711636 0.34805188 -0.32548281 3.5938596 1.708270
## X75 0.26236426 1.26963623 -0.33825519 3.7055056 1.762644
## X76 0.33647224 0.18568645 0.09668586 1.2696362 1.732739
## X77 0.09531018 -0.06149412 0.27296583 3.4760910 1.722650
## X78 0.99325177 -0.09342680 0.50475301 3.4760910 1.779137
## X80 0.40546511 0.09622438 0.00000000 3.9130123 1.687652
## X81 0.47000363 -0.09342680 0.27296583 2.9244660 1.705116
## X82 0.00000000 -1.09654116 0.18739989 2.5785828 1.717157
## X83 0.09531018 0.79981129 0.35404039 3.5938596 1.727834
## X84 -0.15082289 0.00000000 -0.25138068 2.1548454 1.683772
## X85 0.83290912 -1.45166590 -0.03032047 4.0099156 1.725279
## X86 0.91629073 0.18568645 0.50475301 4.3608562 1.760954
## X88 -0.43078292 0.09622438 -0.18121747 2.1548454 1.751931
## X90 -0.56211892 0.00000000 -0.45939334 1.7245084 1.651845
## X93 0.69314718 -1.45166590 0.43115645 3.0769713 1.735094
## X94 0.91629073 0.49280272 0.18739989 4.1028210 1.794804
## X95 0.33647224 0.34805188 -0.07178642 3.7055056 1.705116
## X96 0.64185389 -1.53427578 -0.26344327 2.5785828 1.695003
## X97 0.00000000 -0.61296931 0.64279595 2.1548454 1.695003
## X98 0.00000000 -0.07204658 -0.43144357 2.7592279 1.634852
## X99 0.47000363 0.62373057 -0.12541320 3.0769713 1.767505
## X100 0.33647224 -0.64724718 0.50475301 2.3362105 1.666667
## X103 1.09861229 0.34805188 0.64279595 3.9130123 1.725279
## X104 0.09531018 0.00000000 -0.06130466 3.7055056 1.719944
## X105 0.64185389 -0.70078093 -0.10371280 3.4760910 1.725279
## X107 0.58778666 0.09622438 0.27296583 3.2187591 1.737387
## X108 0.26236426 0.34805188 0.27296583 3.9130123 1.711325
## X109 0.58778666 -0.24238905 0.27296583 2.1548454 1.661938
## X110 0.00000000 -0.50074709 -0.14746164 2.1548454 1.687652
## X111 0.40546511 -0.15990607 -0.16987200 3.5938596 1.714286
## X112 0.83290912 -0.07204658 0.64279595 5.3496753 1.737387
## X113 0.58778666 -0.24238905 0.27296583 3.9130123 1.725279
## X114 -0.17435339 0.09622438 -0.41770114 2.5785828 1.711325
## X115 -0.49429632 -0.18290044 -0.35115595 2.1548454 1.714286
## X117 0.53062825 0.00000000 0.50475301 3.4760910 1.711325
## X118 0.40546511 -0.24238905 0.27296583 2.9244660 1.730320
## X121 -0.15082289 0.95678949 0.64279595 1.2696362 1.711325
## X123 -0.18632958 -0.77658561 -0.19265903 2.1548454 1.657003
## X124 -0.18632958 -0.37116408 0.57519641 3.4760910 1.651845
## X126 0.00000000 0.34805188 -0.13639307 1.2696362 1.705116
## X128 0.47000363 -0.64724718 0.43115645 2.9244660 1.773545
## X129 -0.37106368 -0.51610326 -0.62582535 1.6936607 1.651845
## X130 0.00000000 -0.50074709 -0.05090066 0.5598079 1.687652
## X131 0.18232156 -0.83723396 0.09668586 3.4760910 1.691393
## X132 0.00000000 -0.26704121 0.35404039 2.0567968 1.717157
## X133 0.33647224 -1.09654116 0.09668586 3.4760910 1.671202
## X134 0.47000363 -0.61296931 0.35404039 2.9244660 1.735094
## X135 0.00000000 0.00000000 0.18739989 2.1548454 1.646447
## X136 -0.56211892 0.00000000 -0.16987200 3.7055056 1.698489
## X137 -0.13926207 -0.45589516 -0.21583851 2.3788658 1.661938
## X139 0.00000000 -0.24238905 0.00000000 0.5598079 1.646447
## X140 0.18232156 -1.53427578 -0.11452033 2.9244660 1.711325
## X141 0.33647224 -0.02016195 0.64279595 1.8708303 1.708270
## X143 0.00000000 0.09622438 -0.33825519 3.4760910 1.714286
## X144 0.00000000 0.74349177 -0.10371280 0.5598079 1.675557
## X145 0.18232156 -0.37116408 0.50475301 3.5938596 1.691393
## X146 0.64185389 -0.24238905 0.35404039 3.9130123 1.730320
## X147 0.33647224 0.18568645 0.57519641 2.1548454 1.737387
## X148 0.87546874 0.79981129 -0.27561748 5.0617331 1.769060
## X149 0.33647224 0.26936976 0.18739989 3.4760910 1.717157
## X152 0.99325177 -0.61296931 0.43115645 4.5182697 1.770584
## X153 0.74193734 1.30957344 -0.21583851 3.4760910 1.759228
## X154 0.18232156 -0.24238905 -0.04057305 1.4599700 1.657003
## X155 0.00000000 -0.07204658 0.09668586 2.1548454 1.701858
## X156 0.58778666 0.49280272 -0.06130466 3.7055056 1.739622
## X157 0.47000363 0.49280272 0.27296583 2.7592279 1.687652
## X158 0.09531018 0.26936976 0.18739989 1.8425543 1.691393
## X159 -0.65392647 -1.45166590 0.00000000 2.1548454 1.753817
## X160 0.69314718 -0.07204658 0.27296583 3.9130123 1.687652
## X161 0.18232156 -0.18290044 0.70781531 2.9244660 1.679744
## X162 0.09531018 -1.01892829 0.27296583 3.0769713 1.695003
## X163 -0.56211892 0.00000000 0.09668586 0.7434918 1.679744
## X165 -0.17435339 -1.45166590 0.27296583 0.5598079 1.708270
## X166 -0.05129329 -0.64724718 0.18739989 2.0567968 1.640789
## X167 0.87546874 -0.37116408 0.57519641 4.1920814 1.727834
## X168 1.02961942 -0.45589516 0.27296583 4.1920814 1.708270
## X169 -0.17435339 -0.24238905 0.00000000 2.5785828 1.666667
## X170 -0.40047757 0.26936976 -0.31283574 1.5642169 1.695003
## X171 0.58778666 -0.64724718 0.27296583 4.1920814 1.714286
## X172 0.00000000 -0.50074709 0.35404039 1.4236758 1.671202
## X174 0.33647224 0.42235886 0.09668586 2.0567968 1.719944
## X175 0.33647224 -0.09342680 -0.23942725 3.7055056 1.741801
## X176 0.00000000 -0.39871863 -0.15862068 2.5595409 1.727834
## X177 0.40546511 -0.15990607 0.57519641 4.1028210 1.675557
## X178 -0.37106368 0.00000000 -0.09298900 2.1548454 1.622036
## X179 0.64185389 0.09622438 0.35404039 5.0000000 1.760954
## X180 0.58778666 0.26936976 -0.67562020 2.5785828 1.748024
## X181 -0.09431068 -0.15990607 0.18739989 3.4760910 1.708270
## X182 0.33647224 -0.07204658 -0.31283574 2.0567968 1.698489
## X183 0.18232156 -0.07204658 -0.30031103 3.5938596 1.725279
## X184 0.58778666 -1.53427578 0.18739989 2.1548454 1.705116
## X185 0.26236426 -0.18290044 0.35404039 2.7763945 1.691393
## X186 0.00000000 -0.09342680 0.18739989 1.5642169 1.607768
## X189 -0.17435339 -0.61296931 0.27296583 2.1548454 1.657003
## X190 0.09531018 -1.01892829 0.09668586 2.7763945 1.683772
## X191 0.33647224 -0.81662520 -0.21583851 2.7763945 1.705116
## X192 0.83290912 -0.37116408 0.50475301 3.3513883 1.739622
## X193 0.47000363 -0.51610326 0.27296583 2.5595409 1.698489
## X194 0.26236426 0.00000000 0.18739989 3.9130123 1.666667
## X195 0.87546874 1.00562217 0.18739989 3.7055056 1.772079
## X197 0.58778666 -0.02016195 0.70781531 2.9244660 1.717157
## X198 0.26236426 -0.50074709 0.09668586 2.0567968 1.661938
## X200 0.33647224 0.34805188 0.57519641 3.4760910 1.732739
## X201 -0.09431068 -0.06149412 -0.02014161 3.8117017 1.708270
## X202 -0.43078292 -0.26704121 0.00000000 3.2187591 1.695003
## X205 0.47000363 -0.45589516 0.35404039 2.3788658 1.705116
## X208 0.26236426 -0.15990607 0.35404039 3.0769713 1.675557
## X210 0.09531018 -0.24238905 0.50475301 0.5598079 1.683772
## X212 0.00000000 -0.99435191 0.00000000 2.5595409 1.675557
## X213 0.18232156 -0.41274719 0.18739989 2.1548454 1.691393
## X214 0.18232156 0.85402456 0.50475301 3.2187591 1.806653
## X215 0.33647224 -0.26704121 0.00000000 2.0567968 1.719944
## X216 -0.56211892 0.34805188 -0.08234738 1.7245084 1.727834
## X218 0.09531018 -0.06149412 0.43115645 3.3513883 1.698489
## X219 -0.24846136 0.18568645 0.00000000 2.0567968 1.679744
## X220 0.18232156 -0.39871863 0.27296583 2.5595409 1.666667
## X223 0.33647224 -0.26704121 0.00000000 4.1028210 1.701858
## X224 0.53062825 -0.50074709 0.09668586 2.1548454 1.657003
## X225 0.33647224 -0.26704121 0.50475301 2.0567968 1.714286
## X226 0.78845736 0.49280272 0.57519641 2.3362105 1.711325
## X227 0.64185389 0.00000000 0.27296583 4.3608562 1.727834
## X228 -0.43078292 -0.07204658 -0.18121747 3.2187591 1.679744
## X229 -0.54472718 -0.61296931 -0.07178642 2.1548454 1.695003
## X230 0.00000000 0.55980793 0.09668586 2.1548454 1.719944
## X231 0.18232156 -0.41274719 0.09668586 1.5642169 1.695003
## X232 -0.09431068 0.18568645 0.35404039 3.0769713 1.691393
## X233 -0.30110509 0.00000000 -0.30031103 1.7245084 1.687652
## X234 0.18232156 -0.71923319 -0.07178642 3.4760910 1.675557
## X236 0.18232156 -0.15990607 0.00000000 2.1548454 1.634852
## X237 0.47000363 -0.64724718 0.35404039 3.4760910 1.732739
## X239 -0.18632958 0.79981129 -0.40410820 5.7056368 1.695003
## X240 0.53062825 0.09622438 0.18739989 2.1548454 1.737387
## X241 0.58778666 -1.45166590 0.00000000 3.9130123 1.657003
## X242 0.78845736 0.09622438 0.64279595 5.1219873 1.737387
## X243 -0.15082289 0.09622438 0.09668586 1.2696362 1.661938
## X244 0.00000000 0.79981129 0.18739989 1.2696362 1.797969
## X245 -0.41551544 -0.15990607 -0.67562020 1.7245084 1.687652
## X246 0.74193734 -0.26704121 -0.18121747 4.2780037 1.711325
## X247 -0.41551544 -0.37116408 -0.33825519 2.0567968 1.671202
## X249 0.58778666 -0.24238905 0.83099088 1.5642169 1.687652
## X250 0.18232156 -0.99435191 -0.05090066 3.0769713 1.739622
## X251 -1.04982212 0.18568645 0.18739989 1.2696362 1.691393
## X253 0.47000363 0.18641819 0.43115645 3.4760910 1.750000
## X254 1.16315081 -0.37116408 0.64279595 4.3608562 1.762644
## X255 0.64185389 -0.39871863 0.18739989 2.0567968 1.750000
## X256 0.00000000 -0.64724718 -0.18121747 2.0567968 1.646447
## X257 -0.15082289 -0.81662520 0.00000000 2.0567968 1.628609
## X258 0.18232156 0.90630094 -0.13639307 2.1548454 1.695003
## X260 -0.09431068 -0.64724718 -0.12541320 3.0769713 1.741801
## X261 -0.65392647 -0.27956244 -0.03032047 1.2696362 1.687652
## X262 -0.30110509 -0.15990607 0.18739989 1.7245084 1.666667
## X263 0.69314718 -0.15990607 0.50475301 3.9130123 1.741801
## X264 1.06471074 -0.64724718 -0.22758062 4.5182697 1.753817
## X265 0.33647224 -0.15990607 0.27296583 4.2780037 1.661938
## X267 0.33647224 0.26936976 0.57519641 2.9244660 1.719944
## X268 0.18232156 -0.37116408 0.00000000 3.7055056 1.732739
## X269 0.47000363 0.26936976 -0.19265903 2.7592279 1.657003
## X270 -0.09431068 -0.81662520 0.64279595 2.7763945 1.739622
## X271 0.26236426 -0.37116408 0.35404039 3.4760910 1.711325
## X272 -0.18632958 0.18568645 -0.25138068 3.7055056 1.687652
## X273 -0.30110509 0.68487244 0.57519641 2.3362105 1.746000
## X274 0.40546511 -0.15990607 0.27296583 0.7434918 1.675557
## X275 0.95551145 0.49280272 0.50475301 4.4408751 1.691393
## X277 -0.07257069 -0.41274719 0.18739989 2.5785828 1.717157
## X278 0.64185389 -0.07204658 0.35404039 3.9130123 1.719944
## X279 0.18232156 -1.27337604 0.00000000 2.5785828 1.675557
## X281 0.47000363 -0.09342680 0.35404039 2.5785828 1.705116
## X282 0.33647224 0.34805188 -0.48799216 1.8425543 1.743926
## X283 0.69314718 -0.15990607 0.09668586 3.4760910 1.753817
## X287 0.58778666 0.09622438 0.09668586 3.9130123 1.675557
## X289 -0.18632958 -0.07204658 -0.60969274 3.5938596 1.687652
## X290 -0.37106368 -0.24238905 -0.57808019 2.3788658 1.698489
## X291 0.33647224 0.49280272 0.09668586 3.2187591 1.759228
## X292 0.33647224 1.81380304 0.64279595 3.5938596 1.760954
## X294 0.18232156 -0.26704121 -0.48799216 3.9130123 1.671202
## X297 0.00000000 -0.39871863 0.57519641 1.2696362 1.640789
## X298 0.47000363 -0.24238905 -0.30031103 2.9244660 1.657003
## X299 -0.17435339 -0.83723396 0.27296583 2.1548454 1.675557
## X301 0.18232156 -0.39871863 0.18739989 2.1548454 1.607768
## X302 0.18232156 0.26936976 -0.45939334 2.1548454 1.708270
## X303 0.47000363 0.68487244 -0.12541320 2.3362105 1.717157
## X304 0.18232156 -0.18290044 0.00000000 2.1548454 1.701858
## X305 0.58778666 -0.64724718 -0.08234738 3.7055056 1.711325
## X306 0.58778666 -0.50074709 0.18739989 4.1920814 1.743926
## X307 1.16315081 -0.26704121 0.00000000 4.2780037 1.753817
## X308 0.83290912 0.09622438 0.57519641 4.6659102 1.691393
## X311 0.47000363 -0.39871863 -0.27561748 4.2780037 1.730320
## X312 0.09531018 0.00000000 -0.02014161 2.5595409 1.683772
## X313 -0.30110509 -0.25465110 0.43115645 3.0769713 1.705116
## X314 -0.56211892 -1.45166590 -0.06130466 2.5595409 1.661938
## X315 0.64185389 0.09622438 0.35404039 4.3608562 1.743926
## X316 0.18232156 -0.07204658 0.50475301 4.1028210 1.671202
## X317 0.18232156 -0.45589516 0.35404039 2.7934108 1.646447
## X320 -0.13926207 0.00000000 0.00000000 4.1028210 1.666667
## X321 -0.13926207 -0.07204658 -0.64218542 3.7055056 1.679744
## X322 0.58778666 -0.39871863 0.18739989 4.4408751 1.698489
## X323 0.09531018 0.00000000 -0.27561748 2.7763945 1.675557
## X324 0.18232156 -0.26704121 0.09668586 3.2187591 1.698489
## X325 -0.09431068 0.09622438 0.43115645 4.4408751 1.722650
## X326 -0.10503540 -0.81662520 0.35404039 2.0567968 1.701858
## X327 0.18232156 0.26936976 0.09668586 2.9244660 1.725279
## X329 0.64185389 0.18568645 0.09668586 2.1548454 1.714286
## X330 0.26236426 0.42235886 -0.05090066 3.7055056 1.727834
## X331 -0.56211892 -1.01892829 -0.06130466 3.0769713 1.600000
## X332 -0.09431068 -0.15990607 -0.35115595 3.3513883 1.717157
## X333 0.69314718 -1.27337604 0.43115645 3.9130123 1.727834
## IP_10_Inducible_Protein_10 IgA Insulin
## X1 6.242223 -6.812445 -0.6258253
## X2 5.686975 -6.377127 -0.9431406
## X3 5.049856 -6.319969 -1.4466191
## X5 6.369901 -4.645992 -0.3003110
## X6 5.480639 -5.809143 -1.3405481
## X7 5.451038 -6.645391 -0.8398078
## X8 5.968708 -5.083206 -1.0105157
## X9 5.375278 -6.645391 -1.4852687
## X11 6.144186 -5.776353 -1.3079612
## X12 5.164786 -6.502290 -1.0827874
## X14 6.313548 -5.599422 -1.3405481
## X16 5.598422 -5.449140 -1.4097335
## X17 6.063785 -5.496768 -1.1884825
## X18 5.036953 -6.214608 -1.6677387
## X19 7.383989 -7.323271 -1.3405481
## X20 5.375278 -4.509860 -1.3405481
## X21 6.218600 -5.339139 -1.4466191
## X22 5.789960 -5.403678 -1.1884825
## X23 5.056246 -5.952244 -1.2765538
## X24 5.683580 -6.119298 -0.8203007
## X25 5.416100 -7.402052 -1.2765538
## X26 5.843544 -6.165818 -1.4852687
## X28 5.758902 -6.502290 -0.7456014
## X29 5.480639 -6.437752 -1.3405481
## X30 5.837730 -7.293418 -1.3079612
## X31 6.428105 -6.074846 -0.9431406
## X34 5.484797 -5.278515 -1.1884825
## X35 4.905275 -5.744604 -1.8132213
## X36 6.612041 -4.803621 -0.7638043
## X37 6.086775 -6.074846 -1.2765538
## X38 5.902633 -6.907755 -1.3079612
## X39 5.181784 -6.119298 -0.7823131
## X40 6.527958 -6.119298 -1.5259022
## X41 5.484797 -5.496768 -1.2168953
## X42 5.541264 -6.214608 -1.8652431
## X43 5.937536 -6.265901 -1.4466191
## X44 6.675823 -6.437752 -1.2765538
## X45 6.118097 -5.744604 -1.0827874
## X46 5.204007 -6.571283 -1.2168953
## X47 5.389072 -6.907755 -1.8652431
## X48 5.758902 -6.032287 -0.8799269
## X50 5.723585 -5.572754 -1.1884825
## X51 5.451038 -6.948577 -1.5687868
## X53 5.983936 -5.360193 -1.1081246
## X55 5.476464 -6.725434 -1.5259022
## X56 5.913503 -6.437752 -1.4852687
## X57 6.156979 -5.259097 -1.0827874
## X59 6.565265 -5.472671 -1.8132213
## X60 5.817111 -5.572754 -1.3744281
## X61 5.147494 -6.502290 -1.4466191
## X62 5.817111 -6.265901 -1.3744281
## X63 5.981414 -6.032287 -2.1691668
## X64 6.059123 -5.360193 -1.4097335
## X65 5.129899 -6.645391 -1.3405481
## X67 5.468060 -6.812445 -2.1691668
## X68 5.318120 -6.725434 -1.2462272
## X69 5.225747 -6.119298 -1.3405481
## X70 5.765191 -7.236259 -1.1884825
## X71 5.153292 -7.035589 -1.2765538
## X72 6.565265 -5.201186 -0.4177011
## X73 5.805135 -6.032287 -1.1081246
## X74 5.758902 -5.572754 -2.1464071
## X75 6.115892 -4.791500 -1.0105157
## X76 5.141664 -6.165818 -1.3079612
## X77 5.420535 -5.360193 -1.2462272
## X78 6.700731 -5.744604 -0.9431406
## X80 5.855072 -5.991465 -1.0340201
## X81 5.891644 -7.195437 -1.1341535
## X82 5.846439 -6.502290 -1.2765538
## X83 5.983936 -6.437752 -1.2462272
## X84 5.690359 -6.907755 -1.4852687
## X85 6.359574 -5.776353 -0.9005739
## X86 6.023448 -5.020686 -0.5472920
## X88 5.958425 -6.502290 -1.3744281
## X90 5.505332 -6.645391 -1.4852687
## X93 5.799093 -5.067206 -1.2462272
## X94 6.813445 -4.268698 -0.7276925
## X95 5.697093 -6.725434 -1.0827874
## X96 6.300786 -6.725434 -1.3405481
## X97 5.905362 -6.645391 -2.0098877
## X98 5.327876 -6.927958 -1.5259022
## X99 5.525453 -6.437752 -0.9651041
## X100 5.662960 -6.377127 -1.6142515
## X103 6.115892 -8.047190 -0.8799269
## X104 5.686975 -6.214608 -1.1081246
## X105 5.068904 -6.645391 -0.9431406
## X107 5.361292 -6.725434 -0.9216381
## X108 6.326149 -6.437752 -0.7823131
## X109 5.384495 -6.319969 -1.6677387
## X110 5.752573 -7.250246 -1.4466191
## X111 5.529429 -6.377127 -1.0105157
## X112 6.423247 -5.083206 -0.3641883
## X113 6.322565 -4.699481 -0.8596776
## X114 5.468060 -5.521461 -2.0098877
## X115 6.879356 -4.879607 -1.4852687
## X117 5.793014 -6.812445 -1.0340201
## X118 6.579251 -6.319969 -0.8011407
## X121 5.673323 -5.318520 -1.7093141
## X123 5.451038 -6.502290 -2.1464071
## X124 5.075174 -6.074846 -0.7456014
## X126 5.049856 -6.319969 -1.8132213
## X128 6.651572 -5.914504 -1.4097335
## X129 5.934894 -6.437752 -1.8132213
## X130 5.793014 -5.259097 -1.1884825
## X131 4.962845 -7.082109 -1.1081246
## X132 5.852202 -6.214608 -1.2168953
## X133 6.107023 -5.083206 -0.9875535
## X134 5.743003 -5.472671 -0.9875535
## X135 5.332719 -5.546779 -1.0580989
## X136 6.410175 -5.083206 -1.2765538
## X137 4.934474 -5.713833 -1.2765538
## X139 5.501258 -6.265901 -1.8652431
## X140 6.406880 -6.265901 -1.0827874
## X141 5.429346 -6.645391 -1.2765538
## X143 6.068426 -5.991465 -1.2168953
## X144 6.013715 -6.214608 -1.8652431
## X145 5.513429 -6.032287 -1.3744281
## X146 6.646391 -4.879607 -0.7638043
## X147 6.651572 -6.938214 -1.1884825
## X148 6.588926 -5.952244 -0.2275806
## X149 5.720312 -7.338538 -1.0580989
## X152 6.431331 -5.683980 -0.3003110
## X153 6.165418 -5.099467 -0.8596776
## X154 4.990433 -7.013116 -1.2168953
## X155 5.616771 -6.265901 -1.3079612
## X156 5.472271 -5.240048 -0.8011407
## X157 4.927254 -6.571283 -1.0827874
## X158 4.997212 -5.914504 -1.4466191
## X159 6.259581 -5.914504 -1.4852687
## X160 4.962845 -5.496768 -0.8011407
## X161 6.444131 -5.952244 -1.2462272
## X162 5.846439 -7.118476 -1.2765538
## X163 5.641907 -6.907755 -1.4466191
## X165 6.184149 -6.645391 -1.7255647
## X166 5.613128 -6.571283 -1.4852687
## X167 6.220590 -6.907755 -0.8398078
## X168 5.111988 -6.725434 -0.7823131
## X169 5.575949 -5.599422 -1.2765538
## X170 6.336826 -5.952244 -1.7255647
## X171 5.899897 -5.149897 -1.1884825
## X172 5.389072 -6.645391 -1.1341535
## X174 5.648974 -5.521461 -1.2168953
## X175 5.828946 -4.625373 -1.0340201
## X176 6.169611 -6.437752 -1.4852687
## X177 5.198497 -6.812445 -0.9651041
## X178 5.897154 -6.907755 -1.8652431
## X179 6.073045 -4.828314 -0.8011407
## X180 6.220738 -6.119298 -1.1609217
## X181 5.135798 -6.571283 -1.4097335
## X182 5.214936 -10.519674 -1.0827874
## X183 4.990433 -5.809143 -1.0827874
## X184 5.739793 -6.812445 -1.0827874
## X185 5.863631 -5.809143 -1.4097335
## X186 4.779123 -6.265901 -1.4466191
## X189 5.720312 -5.878136 -1.3405481
## X190 5.968708 -5.654992 -1.2462272
## X191 5.575949 -6.119298 -1.3405481
## X192 6.315358 -6.165818 -0.9651041
## X193 5.752573 -7.082109 -1.1341535
## X194 5.493061 -6.165818 -1.0340201
## X195 5.805135 -6.165818 -0.7456014
## X197 5.777652 -5.449140 -1.1341535
## X198 5.958425 -6.725434 -1.1341535
## X200 5.549076 -6.725434 -1.2462272
## X201 6.063785 -6.165818 -1.2765538
## X202 5.659482 -6.991137 -1.4466191
## X205 5.135798 -5.878136 -1.1081246
## X208 6.903747 -6.265901 -1.0340201
## X210 6.001415 -6.502290 -1.7255647
## X212 5.407172 -7.799353 -1.4852687
## X213 5.424950 -5.713833 -1.1341535
## X214 5.298317 -6.319969 -0.8596776
## X215 5.278115 -6.571283 -1.3079612
## X216 5.934894 -5.878136 -1.4852687
## X218 6.456770 -4.840893 -0.8596776
## X219 5.986452 -6.119298 -1.8652431
## X220 5.361292 -7.452482 -1.0827874
## X223 5.442418 -5.298317 -0.9005739
## X224 5.733341 -5.713833 -1.0105157
## X225 5.752573 -6.377127 -1.1341535
## X226 5.575949 -5.521461 -1.2462272
## X227 5.996452 -5.318520 -0.9005739
## X228 5.652489 -6.812445 -1.2168953
## X229 5.648974 -5.221356 -1.4852687
## X230 5.579730 -5.426151 -1.2765538
## X231 5.863631 -6.502290 -1.5687868
## X232 5.384495 -5.599422 -1.1341535
## X233 5.937536 -7.106206 -1.5687868
## X234 4.969813 -5.184989 -1.0827874
## X236 5.198497 -6.812445 -1.7255647
## X237 5.953243 -6.980326 -0.9875535
## X239 5.030438 -5.843045 -1.5259022
## X240 5.288267 -7.523941 -1.1884825
## X241 5.288267 -6.319969 -1.0827874
## X242 5.537334 -4.342806 -0.1586207
## X243 5.093750 -5.683980 -1.6142515
## X244 5.902633 -6.645391 -1.6142515
## X245 4.700480 -6.377127 -1.3405481
## X246 6.298949 -4.947660 -0.7638043
## X247 5.817111 -6.645391 -1.7708847
## X249 5.590987 -7.182192 -1.1884825
## X250 5.746203 -5.914504 -1.1609217
## X251 5.686975 -6.571283 -1.4097335
## X253 6.003887 -4.976234 -1.2765538
## X254 6.793466 -5.914504 -0.8398078
## X255 5.993961 -5.184989 -1.2462272
## X256 4.941642 -6.907755 -1.8652431
## X257 5.123964 -7.293418 -1.4852687
## X258 5.323010 -6.927958 -1.2765538
## X260 5.872118 -5.878136 -1.1081246
## X261 5.648974 -6.265901 -1.8132213
## X262 4.844187 -6.074846 -1.3405481
## X263 6.077642 -7.082109 -0.7638043
## X264 6.070738 -5.472671 -0.6756202
## X265 5.303305 -5.713833 -1.2462272
## X267 6.063785 -5.599422 -1.1884825
## X268 6.013715 -6.725434 -1.1884825
## X269 5.087596 -8.145630 -1.6677387
## X270 5.620401 -5.843045 -1.2462272
## X271 5.924256 -5.472671 -1.2168953
## X272 5.820083 -5.381699 -1.2765538
## X273 6.173786 -6.119298 -1.2765538
## X274 5.587249 -6.502290 -1.4466191
## X275 6.122493 -5.991465 -0.7823131
## X277 6.137727 -5.683980 -1.3405481
## X278 6.357842 -5.878136 -0.8398078
## X279 6.186209 -5.572754 -1.3405481
## X281 5.631212 -5.221356 -1.0340201
## X282 5.356586 -5.991465 -0.9431406
## X283 6.873164 -5.744604 -1.2462272
## X287 5.135798 -5.809143 -0.9651041
## X289 5.501258 -5.952244 -1.3079612
## X290 5.517453 -5.991465 -1.4466191
## X291 5.389072 -5.381699 -1.2765538
## X292 6.617403 -6.265901 -1.0827874
## X294 5.840642 -6.571283 -1.0340201
## X297 5.517453 -5.952244 -1.4097335
## X298 4.317488 -6.119298 -1.0580989
## X299 6.098074 -6.725434 -1.4466191
## X301 5.590987 -7.542634 -1.9446703
## X302 5.241747 -6.165818 -1.1341535
## X303 5.720312 -6.119298 -1.2462272
## X304 5.796058 -6.437752 -1.8132213
## X305 5.361292 -5.713833 -1.0827874
## X306 5.497168 -5.472671 -1.0340201
## X307 6.532334 -5.546779 -0.6421854
## X308 6.054439 -5.521461 -0.6096927
## X311 6.278521 -5.221356 -1.0827874
## X312 5.874931 -6.032287 -1.4852687
## X313 6.059123 -6.502290 -1.3405481
## X314 5.087596 -7.035589 -1.9145792
## X315 6.208590 -5.339139 -0.7823131
## X316 4.356709 -6.119298 -1.0580989
## X317 5.971262 -6.907755 -1.1341535
## X320 5.252273 -4.710531 -1.3405481
## X321 5.111988 -7.024289 -1.2462272
## X322 6.011267 -5.051457 -0.6421854
## X323 6.897705 -6.214608 -1.4852687
## X324 6.208590 -7.169120 -1.1341535
## X325 7.501082 -6.319969 -0.9651041
## X326 5.560682 -6.812445 -1.5687868
## X327 5.587249 -5.240048 -1.2168953
## X329 5.926926 -6.812445 -1.2765538
## X330 5.267858 -4.199705 -1.3744281
## X331 5.293305 -6.265901 -1.5687868
## X332 5.273000 -5.914504 -1.2462272
## X333 6.746412 -6.074846 -1.0340201
## Kidney_Injury_Molecule_1_KIM_1 LOX_1 Leptin Lipoprotein_a MCP_1
## X1 -1.204295 1.7047481 -1.5290628 -4.268698 6.740519
## X2 -1.197703 1.5260563 -1.4660558 -4.933674 6.849066
## X3 -1.191191 1.1631508 -1.6622675 -5.843045 6.767343
## X5 -1.163800 1.3609766 -0.9151068 -2.937463 6.722630
## X6 -1.123868 0.6418539 -1.3613475 -4.509860 6.541030
## X7 -1.143534 1.2237754 -1.7051413 -6.319969 6.359574
## X8 -1.184754 1.4350845 -1.7517987 -3.863233 6.448889
## X9 -1.159695 1.0986123 -1.7833269 -4.961845 6.445720
## X11 -1.155616 1.4816045 -1.1867361 -5.572754 6.606650
## X12 -1.153587 1.4586150 -1.9987562 -5.083206 6.444131
## X14 -1.172093 1.0647107 -1.5087252 -3.442019 6.744059
## X16 -1.202089 1.8405496 -1.3294863 -5.914504 6.212606
## X17 -1.123868 0.5306283 -1.6420151 -2.120264 6.781058
## X18 -1.163800 0.9162907 -1.6832823 -6.032287 6.501290
## X19 -1.206511 0.9162907 -1.7397296 -3.540459 6.066108
## X20 -1.191191 0.8754687 -1.6622675 -2.302585 6.787845
## X21 -1.147537 0.7419373 -1.3294863 -6.032287 6.513230
## X22 -1.224597 1.8718022 -1.2134062 -2.995732 6.476972
## X23 -1.186891 0.9162907 -1.3294863 -3.863233 6.293419
## X24 -1.202089 1.2809338 -1.7221470 -5.035953 6.403574
## X25 -1.155616 0.9555114 -1.5047321 -5.599422 6.424869
## X26 -1.155616 1.4109870 -1.7221470 -4.733004 6.122493
## X28 -1.182624 1.0296194 -1.7457280 -3.324236 7.003065
## X29 -1.184754 1.0986123 -1.4294363 -4.135167 6.484635
## X30 -1.143534 1.0647107 -1.6272839 -4.017384 6.177944
## X31 -1.145532 0.9932518 -1.3294863 -4.645992 6.408529
## X34 -1.167932 0.9555114 -1.2134062 -4.342806 6.815640
## X35 -1.170009 0.5306283 -1.9987562 -5.083206 6.478510
## X36 -1.174184 1.5260563 -1.7517987 -2.207275 6.651572
## X37 -1.182624 1.2527630 -1.9877978 -4.074542 6.293419
## X38 -1.172093 1.5260563 -1.3613475 -5.184989 6.416732
## X39 -1.199892 1.5892352 -1.7164169 -4.645992 6.302619
## X40 -1.172093 1.4350845 -1.4660558 -6.812445 6.797940
## X41 -1.159695 1.1939225 -1.5673739 -4.199705 6.796824
## X42 -1.213217 1.4586150 -1.5127425 -5.472671 6.246107
## X43 -1.123868 1.0296194 -1.9471197 -3.352407 6.364751
## X44 -1.210972 1.5260563 -1.3946054 -4.635629 6.583409
## X45 -1.155616 1.4586150 -1.8032876 -5.914504 6.877296
## X46 -1.217737 1.2809338 -1.5988899 -3.649659 6.602588
## X47 -1.186891 1.1939225 -1.7221470 -4.667046 6.516193
## X48 -1.165862 1.5040774 -1.3294863 -5.599422 6.562444
## X50 -1.213217 1.7404662 -1.2409325 -5.278515 6.453625
## X51 -1.224597 1.6863990 -1.7965395 -5.221356 6.154858
## X53 -1.199892 1.0647107 -1.3294863 -4.268698 6.401917
## X55 -1.204295 1.0986123 -1.2988756 -5.318520 6.222576
## X56 -1.174184 1.3609766 -1.8170863 -4.803621 6.480045
## X57 -1.231557 1.3350011 -1.7107489 -4.645992 6.661855
## X59 -1.191191 1.2809338 -1.7221470 -4.688552 6.565265
## X60 -1.184754 0.8329091 -1.7338014 -5.449140 6.481577
## X61 -1.163800 0.9555114 -1.1608546 -5.449140 6.242223
## X62 -1.143534 0.6931472 -1.2693924 -5.472671 6.556778
## X63 -1.182624 1.5892352 -1.6995924 -5.381699 6.823286
## X64 -1.157652 1.5892352 -1.2988756 -2.847312 6.669498
## X65 -1.197703 1.2237754 -1.4660558 -5.496768 6.320768
## X67 -1.208737 1.9169226 -1.7517987 -3.442019 6.401917
## X68 -1.224597 1.1939225 -1.6520506 -4.342806 6.368187
## X69 -1.170009 0.5877867 -1.2409325 -5.259097 6.263398
## X70 -1.159695 1.0986123 -1.5762143 -5.843045 6.259581
## X71 -1.186891 1.4109870 -1.2693924 -5.546779 6.579251
## X72 -1.231557 1.9740810 -0.6206034 -3.164404 6.603944
## X73 -1.193353 1.5892352 -1.0641271 -4.721704 6.406880
## X74 -1.145532 0.3364722 -1.2988756 -4.815891 6.326149
## X75 -1.204295 0.8754687 -1.5415785 -2.847312 6.946976
## X76 -1.193353 1.9021075 -1.7221470 -5.360193 6.907755
## X77 -1.217737 1.6486586 -1.4294363 -3.649659 6.606650
## X78 -1.195524 1.2527630 -1.5249442 -3.146555 6.293419
## X80 -1.143534 1.1939225 -1.1608546 -4.840893 6.817831
## X81 -1.178389 1.3862944 -1.1608546 -5.259097 6.742881
## X82 -1.172093 1.1631508 -1.4660558 -3.146555 6.630683
## X83 -1.208737 1.2237754 -1.6370627 -3.688879 6.442540
## X84 -1.163800 1.1939225 -1.0873827 -6.165818 6.576470
## X85 -1.197703 1.7047481 -1.5630004 -6.265901 6.767343
## X86 -1.182624 1.4109870 -1.3294863 -3.170086 6.829794
## X88 -1.199892 1.1314021 -1.6832823 -3.170086 6.508769
## X90 -1.241005 0.0000000 -1.8101352 -5.403678 6.586172
## X93 -1.241005 1.7227666 -2.0809951 -5.472671 6.665684
## X94 -1.213217 2.0918641 -1.6224555 -3.442019 7.038784
## X95 -1.104733 1.1939225 -1.6622675 -3.146555 6.517671
## X96 -1.191191 1.0647107 -1.0414230 -4.605170 6.437752
## X97 -1.189037 1.3083328 -1.6420151 -5.360193 6.152733
## X98 -1.167932 0.5306283 -1.3294863 -5.572754 6.459904
## X99 -1.204295 1.5475625 -1.1357025 -3.146555 6.927558
## X100 -1.186891 1.9600948 -1.2134062 -5.878136 6.249975
## X103 -1.213217 1.8405496 -1.3294863 -4.605170 6.726233
## X104 -1.217737 1.8245493 -1.7641672 -3.381395 7.229839
## X105 -1.213217 1.5260563 -1.6995924 -5.654992 6.463029
## X107 -1.161744 1.2527630 -1.3294863 -3.963316 6.161207
## X108 -1.206511 1.0986123 -1.8770048 -4.342806 6.731018
## X109 -1.193353 0.8754687 -1.2693924 -3.963316 6.304449
## X110 -1.157652 0.7419373 -1.3294863 -5.381699 6.869014
## X111 -1.208737 1.5040774 -1.3946054 -3.506558 6.566672
## X112 -1.184754 1.9459101 -1.4294363 -2.796881 6.519147
## X113 -1.159695 1.2809338 -1.5249442 -2.207275 6.748760
## X114 -1.151564 0.6418539 -1.2693924 -5.521461 6.455199
## X115 -1.167932 1.1939225 -1.6674468 -3.411248 6.212606
## X117 -1.161744 1.7917595 -1.3946054 -4.710531 6.184149
## X118 -1.178389 1.7404662 -1.4660558 -5.472671 6.403574
## X121 -1.206511 1.9021075 -1.3613475 -3.381395 6.142037
## X123 -1.170009 0.7419373 -1.5806825 -4.268698 6.663133
## X124 -1.197703 1.7578579 -0.8954783 -5.318520 6.473891
## X126 -1.165862 1.7404662 -1.2988756 -3.772261 6.538140
## X128 -1.182624 1.3862944 -1.3946054 -3.775670 6.927558
## X129 -1.204295 1.0986123 -1.7221470 -2.733368 5.826000
## X130 -1.178389 1.4350845 -1.5047321 -3.963316 6.251904
## X131 -1.180503 1.0986123 -1.1357025 -4.919881 6.356108
## X132 -1.159695 1.0296194 -1.9104977 -5.259097 5.908083
## X133 -1.202089 0.9162907 -2.0809951 -3.273000 6.146329
## X134 -1.149547 1.1939225 -1.2988756 -2.375156 6.844815
## X135 -1.159695 1.5686159 -1.5047321 -5.744604 6.458338
## X136 -1.208737 1.1314021 -1.5087252 -3.270169 6.511745
## X137 -1.161744 1.1939225 -0.9975390 -3.381395 6.084499
## X139 -1.172093 0.7419373 -1.6779529 -5.221356 6.416732
## X140 -1.182624 1.1939225 -1.6571358 -5.878136 6.284134
## X141 -1.145532 1.1314021 -1.2988756 -5.240048 6.364751
## X143 -1.161744 0.4054651 -1.6321526 -3.015935 6.775366
## X144 -1.182624 0.5306283 -1.4294363 -2.659260 6.561031
## X145 -1.174184 1.4109870 -1.4294363 -5.776353 6.493754
## X146 -1.182624 1.3083328 -2.0738009 -2.302585 6.677083
## X147 -1.210972 1.2237754 -1.5373794 -3.270169 6.678342
## X148 -1.229225 1.2809338 -1.1867361 -3.317314 6.613384
## X149 -1.202089 1.0986123 -1.0192371 -4.268698 6.520621
## X152 -1.206511 1.8405496 -2.0738009 -4.017384 6.669498
## X153 -1.172093 0.9932518 -1.4294363 -2.120264 6.669498
## X154 -1.204295 1.0296194 -1.1867361 -4.625373 6.196444
## X155 -1.161744 1.2237754 -1.1608546 -3.473768 6.504288
## X156 -1.191191 1.3862944 -1.0641271 -4.879607 6.532334
## X157 -1.170009 1.4109870 -1.3946054 -4.688552 6.357842
## X158 -1.199892 1.2809338 -1.5806825 -3.442019 6.202536
## X159 -1.174184 1.1314021 -1.3294863 -4.135167 5.905362
## X160 -1.199892 2.0412203 -1.5673739 -5.298317 5.968708
## X161 -1.157652 1.6292405 -1.1867361 -3.079114 5.937536
## X162 -1.224597 1.7749524 -1.2409325 -3.816713 6.689599
## X163 -1.217737 1.3350011 -1.3294863 -6.265901 6.563856
## X165 -1.191191 0.9555114 -1.9877978 -3.649659 7.106606
## X166 -1.180503 1.5260563 -1.2693924 -3.772261 6.529419
## X167 -1.243398 2.2721259 -1.4660558 -4.199705 6.610696
## X168 -1.184754 1.4109870 -1.4660558 -4.342806 6.797940
## X169 -1.170009 1.1314021 -1.4660558 -3.912023 6.371612
## X170 -1.155616 0.5877867 -1.6082042 -4.422849 7.012115
## X171 -1.178389 1.6677068 -1.7833269 -3.381395 6.257668
## X172 -1.147537 1.1314021 -1.4294363 -4.919881 5.940171
## X174 -1.159695 1.2237754 -1.7517987 -2.956512 6.937314
## X175 -1.195524 1.2809338 -1.6779529 -1.386294 6.590301
## X176 -1.163800 1.3350011 -1.3946054 -4.933674 6.697034
## X177 -1.204295 1.9169226 -1.2988756 -5.099467 6.714171
## X178 -1.170009 1.4350845 -1.5897179 -4.947660 6.047372
## X179 -1.123868 0.9162907 -2.0809951 -3.170086 6.347389
## X180 -1.167932 1.0296194 -1.4294363 -6.165818 6.369901
## X181 -1.224597 1.0296194 -1.8313164 -5.626821 6.626718
## X182 -1.170009 0.9932518 -1.8770048 -4.342806 6.200509
## X183 -1.123868 0.9932518 -1.8313164 -4.135167 6.621406
## X184 -1.191191 1.2809338 -1.7221470 -6.214608 6.333280
## X185 -1.174184 1.4816045 -1.1608546 -2.302585 6.778785
## X186 -1.151564 0.9932518 -1.7457280 -2.937463 6.729824
## X189 -1.199892 0.9555114 -1.6571358 -5.298317 6.016157
## X190 -1.224597 1.3609766 -1.8690427 -5.083206 6.429719
## X191 -1.236256 1.0296194 -1.5415785 -3.381395 6.556778
## X192 -1.243398 2.1633230 -1.7397296 -3.816713 7.012115
## X193 -1.202089 1.9315214 -1.2988756 -5.599422 6.214608
## X194 -1.206511 0.9162907 -1.5087252 -4.199705 6.320768
## X195 -1.204295 1.9459101 -1.2693924 -4.767689 6.701960
## X197 -1.163800 1.4816045 -1.1867361 -5.809143 6.366470
## X198 -1.222299 1.4350845 -1.2693924 -3.688879 6.212606
## X200 -1.202089 1.2527630 -1.8770048 -5.167289 6.297109
## X201 -1.184754 1.2527630 -1.4660558 -5.496768 6.345636
## X202 -1.163800 0.8329091 -1.3294863 -4.017384 6.246107
## X205 -1.193353 1.4350845 -1.6886644 -4.961845 6.937314
## X208 -1.213217 1.3350011 -1.3294863 -4.767689 6.075346
## X210 -1.202089 1.1939225 -1.5373794 -5.132803 6.504288
## X212 -1.172093 1.1631508 -1.1867361 -5.051457 6.629363
## X213 -1.172093 1.2237754 -1.5047321 -3.772261 6.637258
## X214 -1.193353 1.3862944 -0.6516715 -3.506558 6.685861
## X215 -1.161744 1.4350845 -2.0466943 -4.199705 6.622736
## X216 -1.197703 1.1631508 -1.9987562 -2.513306 6.513230
## X218 -1.206511 1.4109870 -1.7641672 -3.473768 6.393591
## X219 -1.174184 1.3862944 -1.4294363 -3.473768 6.089045
## X220 -1.186891 1.3350011 -1.4294363 -5.914504 6.679599
## X223 -1.143534 1.3083328 -1.3613475 -4.199705 6.156979
## X224 -1.151564 0.9932518 -1.4660558 -4.933674 6.212606
## X225 -1.197703 1.6292405 -1.4660558 -5.403678 6.253829
## X226 -1.243398 2.2512918 -1.6470107 -5.099467 6.839476
## X227 -1.157652 1.2809338 -1.6272839 -3.575551 6.184149
## X228 -1.143534 1.1314021 -1.0414230 -4.509860 6.434547
## X229 -1.143534 0.2623643 -1.2409325 -5.472671 6.042633
## X230 -1.172093 1.1314021 -2.0738009 -4.422849 6.816736
## X231 -1.123868 0.8329091 -1.6420151 -2.780621 6.282267
## X232 -1.206511 1.3862944 -1.7965395 -3.473768 6.898715
## X233 -1.208737 1.2809338 -1.1608546 -5.360193 6.214608
## X234 -1.147537 1.0296194 -1.9193301 -2.688248 6.154858
## X236 -1.159695 1.3862944 -1.4660558 -6.032287 6.493754
## X237 -1.193353 1.0647107 -1.8170863 -4.422849 6.447306
## X239 -1.149547 0.5306283 -1.2693924 -6.119298 7.012115
## X240 -1.163800 0.6418539 -1.0414230 -5.203007 6.089045
## X241 -1.163800 1.2809338 -1.2134062 -3.649659 6.018593
## X242 -1.199892 0.6418539 -1.1608546 -1.427116 6.862758
## X243 -1.182624 1.3862944 -1.2409325 -4.755993 6.739337
## X244 -1.165862 1.6094379 -1.4294363 -3.015935 6.693324
## X245 -1.224597 0.3364722 -1.5762143 -4.422849 6.405228
## X246 -1.167932 0.9932518 -1.3613475 -4.990833 6.684612
## X247 -1.220013 1.1314021 -1.2134062 -5.449140 6.248043
## X249 -1.182624 1.8405496 -1.3613475 -4.933674 6.580639
## X250 -1.213217 1.0986123 -1.1608546 -4.828314 6.608001
## X251 -1.176283 1.3083328 -1.5047321 -6.119298 6.363028
## X253 -1.143534 1.7047481 -1.3946054 -4.342806 6.385194
## X254 -1.184754 2.0541237 -1.0873827 -5.626821 6.376727
## X255 -1.182624 2.1400662 -1.6370627 -2.364460 6.690842
## X256 -1.195524 1.4350845 -1.6995924 -5.744604 6.317165
## X257 -1.153587 0.8754687 -1.4294363 -4.342806 6.424869
## X258 -1.199892 1.5475625 -1.2409325 -4.074542 6.864848
## X260 -1.206511 1.6863990 -1.6622675 -2.688248 6.553933
## X261 -1.174184 0.8754687 -1.5249442 -6.165818 6.620073
## X262 -1.208737 1.0296194 -1.8934045 -3.649659 6.269096
## X263 -1.220013 1.2527630 -1.2988756 -4.199705 6.439350
## X264 -1.202089 1.6292405 -1.9987562 -4.767689 6.892642
## X265 -1.213217 1.3350011 -1.7965395 -5.426151 6.182085
## X267 -1.178389 1.2809338 -1.7704700 -5.713833 6.568078
## X268 -1.224597 1.7917595 -0.8954783 -2.882404 6.530878
## X269 -1.178389 1.1314021 -1.3294863 -4.688552 6.787845
## X270 -1.233900 1.9600948 -2.1468457 -5.099467 6.525030
## X271 -1.202089 2.0014800 -1.6520506 -3.270169 6.674561
## X272 -1.191191 1.2809338 -1.3946054 -5.572754 6.656727
## X273 -1.231557 1.8245493 -1.6272839 -6.437752 6.582025
## X274 -1.217737 1.2237754 -1.3946054 -5.426151 6.652863
## X275 -1.161744 1.1939225 -1.5415785 -3.912023 6.975414
## X277 -1.167932 0.8754687 -1.8770048 -5.914504 6.419995
## X278 -1.161744 1.0986123 -1.4660558 -5.449140 6.593045
## X279 -1.167932 1.1314021 -1.2988756 -3.540459 6.469250
## X281 -1.151564 0.7884574 -1.3946054 -4.422849 6.697034
## X282 -1.193353 0.4700036 -0.8572661 -3.015935 6.734592
## X283 -1.255574 2.2300144 -1.9987562 -5.426151 6.632002
## X287 -1.191191 1.6292405 -1.2409325 -4.866535 6.366470
## X289 -1.143534 0.7884574 -1.5673739 -5.683980 6.120297
## X290 -1.204295 1.0647107 -1.0414230 -3.079114 7.047517
## X291 -1.167932 0.8329091 -1.2988756 -3.816713 6.317165
## X292 -1.178389 1.6292405 -1.8770048 -5.020686 6.464588
## X294 -1.104733 0.8754687 -1.3294863 -4.767689 6.975414
## X297 -1.176283 1.3862944 -1.2988756 -4.605170 6.045005
## X298 -1.176283 0.9932518 -1.6176666 -3.411248 6.450470
## X299 -1.172093 1.3609766 -1.6571358 -5.713833 5.826000
## X301 -1.174184 1.1631508 -1.4660558 -4.779524 6.625392
## X302 -1.143534 0.4054651 -1.5415785 -5.381699 6.481577
## X303 -1.202089 1.9600948 -1.8313164 -4.268698 6.755769
## X304 -1.186891 2.0412203 -1.5415785 -5.626821 6.519147
## X305 -1.231557 1.7917595 -2.0599345 -5.020686 6.445720
## X306 -1.161744 1.8405496 -1.5673739 -3.218876 6.356108
## X307 -1.174184 1.5686159 -1.3946054 -5.005648 6.699500
## X308 -1.174184 1.2237754 -1.4660558 -2.590267 6.716595
## X311 -1.174184 1.1314021 -1.5762143 -2.995732 6.376727
## X312 -1.208737 1.6292405 -1.4294363 -3.506558 6.527958
## X313 -1.206511 1.6094379 -1.5988899 -4.422849 6.255750
## X314 -1.208737 2.0014800 -1.5586574 -6.377127 5.958425
## X315 -1.178389 0.8329091 -1.6622675 -3.729701 6.481577
## X316 -1.213217 1.0647107 -1.3946054 -4.422849 6.089045
## X317 -1.193353 1.3862944 -1.3946054 -5.572754 7.146772
## X320 -1.172093 0.8329091 -1.2988756 -1.427116 6.184149
## X321 -1.167932 0.5877867 -1.3613475 -2.975930 6.687109
## X322 -1.186891 1.5686159 -1.8170863 -4.828314 6.661855
## X323 -1.226905 1.5892352 -1.8313164 -3.057608 6.440947
## X324 -1.147537 1.0986123 -1.6272839 -5.298317 6.393591
## X325 -1.222299 1.3083328 -1.2693924 -4.947660 6.543912
## X326 -1.248226 1.6863990 -1.9987562 -5.221356 6.075346
## X327 -1.195524 0.9932518 -1.5167845 -4.976234 6.493754
## X329 -1.193353 1.4350845 -1.3946054 -5.149897 6.648985
## X330 -1.155616 0.9162907 -1.6082042 -2.617296 6.699500
## X331 -1.206511 1.1939225 -1.8690427 -4.342806 6.375025
## X332 -1.189037 0.8754687 -1.3613475 -5.051457 6.218600
## X333 -1.253110 2.0412203 -1.7107489 -5.051457 6.472346
## MCP_2 MIF MIP_1alpha MIP_1beta MMP_2 MMP_3 MMP10
## X1 1.9805094 -1.2378744 4.9684528 3.258097 4.47856632 -2.2072749 -3.270169
## X2 1.8088944 -1.8971200 3.6901597 3.135494 3.78147319 -2.4651040 -3.649659
## X3 0.4005958 -2.3025851 4.0495083 2.397895 2.86663136 -2.3025851 -2.733368
## X5 2.2208309 -1.8971200 6.4527639 3.526361 3.69015975 -1.5606477 -2.617296
## X6 2.3343863 -2.0402208 4.6034206 2.890372 2.91775974 -3.0365543 -3.324236
## X7 2.1030230 -2.1202635 3.5512079 2.564949 3.26560115 -2.1202635 -4.135167
## X8 2.6867663 -1.7719568 6.4527639 2.833213 1.55530341 -2.5257286 -3.688879
## X9 1.8527528 -2.2072749 2.1623278 2.219203 2.81511562 -2.5639499 -4.017384
## X11 4.0237466 -1.5141277 5.3589486 4.007333 3.26560115 -2.3025851 -3.963316
## X12 1.5303762 -1.7147984 3.9611107 2.639057 2.10460236 -2.3025851 -3.244194
## X14 2.4440754 -2.0402208 4.9684528 2.833213 3.16792675 -2.5133061 -3.575551
## X16 1.0483341 -1.5141277 4.4785663 2.564949 2.33213462 -2.6592600 -3.123566
## X17 2.8501989 -1.9661129 5.3589486 3.178054 3.26560115 -3.2188758 -3.411248
## X18 1.8527528 -2.3330443 2.7632020 2.639057 2.33213462 -2.3025851 -3.963316
## X19 2.8501989 -1.7147984 5.7354768 2.639057 2.65813701 -3.1941832 -4.074542
## X20 1.5303762 -2.3538784 4.9684528 2.890372 4.60342060 -1.9661129 -2.563950
## X21 2.8501989 -1.4696760 4.3519974 3.091042 3.26560115 -2.3859667 -3.324236
## X22 1.7643559 -1.4696760 4.9285621 2.639057 4.09336677 -1.1711830 -3.611918
## X23 1.8088944 -2.1202635 3.6901597 3.583519 4.04950827 -2.1202635 -4.135167
## X24 1.0483341 -1.7147984 4.6034206 2.833213 4.13700376 -2.7333680 -3.381395
## X25 2.1820549 -2.1202635 3.4097438 2.397895 3.87177749 -2.6736488 -3.506558
## X26 2.0219013 -1.5606477 4.0495083 2.944439 2.55134197 -1.8325815 -3.381395
## X28 1.6263611 -1.8971200 3.7359451 2.116256 3.26560115 -2.5902672 -3.381395
## X29 2.2969819 -1.8971200 3.5043402 2.564949 2.33213462 -2.9004221 -3.772261
## X30 2.1030230 -2.4079456 2.3321346 2.944439 2.81511562 -2.3126354 -3.863233
## X31 2.5152196 -2.1202635 5.3589486 3.688879 3.69015975 -2.3644605 -3.244194
## X34 2.7530556 -1.8971200 3.4097438 3.258097 2.33213462 -2.6592600 -3.270169
## X35 1.5303762 -1.8325815 3.4097438 2.564949 2.33213462 -2.5383074 -3.506558
## X36 1.8527528 -1.8325815 3.9611107 2.944439 1.86752075 -2.0402208 -3.218876
## X37 1.6263611 -1.4696760 4.3519974 2.944439 3.87177749 -1.6094379 -3.218876
## X38 1.8527528 -1.7147984 3.9611107 3.367296 3.21692675 -2.1202635 -3.270169
## X39 0.4005958 -1.8325815 3.2656012 1.945910 4.84829388 -2.6310892 -4.074542
## X40 2.6191813 -2.1202635 3.6901597 2.944439 2.49726361 -2.3025851 -3.912023
## X41 1.0483341 -1.5141277 2.7632020 2.995732 2.33213462 -2.7181005 -3.270169
## X42 1.0483341 -1.7719568 3.4097438 2.944439 2.33213462 -2.4889147 -3.816713
## X43 1.8527528 -2.2072749 3.8717775 2.186051 2.81511562 -2.4889147 -3.218876
## X44 1.8088944 -1.8971200 4.2236285 2.564949 4.04950827 -2.5133061 -3.123566
## X45 3.0369315 -1.3470736 5.3589486 3.218876 3.26560115 -1.4696760 -2.764621
## X46 1.1637797 -1.8325815 3.6901597 3.178054 4.04950827 -1.5141277 -3.270169
## X47 1.8527528 -1.8325815 3.9611107 2.240710 2.33213462 -3.2441936 -3.649659
## X48 1.5303762 -1.1711830 3.5512079 2.890372 3.69015975 -1.9661129 -3.816713
## X50 3.2434918 -1.6607312 4.2236285 3.044522 1.86752075 -2.3025851 -2.645075
## X51 1.7643559 -1.9661129 3.0188940 2.230014 3.55120786 -2.4534080 -4.074542
## X53 1.1637797 -1.8971200 5.3589486 2.833213 2.86663136 -2.3227878 -3.411248
## X55 1.6731213 -2.1202635 4.0495083 2.564949 3.50434016 -2.2072749 -3.540459
## X56 1.5303762 -1.8325815 3.1185934 2.833213 2.81511562 -2.5383074 -3.729701
## X57 1.9805094 -1.2729657 6.7959273 2.186051 4.60342060 -1.9661129 -3.442019
## X59 2.5848812 -1.7147984 4.6857433 2.944439 2.60496200 -2.3644605 -3.772261
## X60 1.6731213 -2.2072749 4.7266389 2.995732 2.55134197 -3.3813948 -4.074542
## X61 1.5303762 -2.1202635 2.7632020 2.639057 2.04628168 -3.2968374 -4.017384
## X62 1.8527528 -2.3538784 3.5512079 2.639057 2.33213462 -3.0791139 -4.422849
## X63 2.3713615 -1.5606477 3.9611107 2.240710 1.86752075 -2.5902672 -3.688879
## X64 2.1427912 -1.6607312 5.3589486 3.367296 1.08050280 -3.3813948 -3.101093
## X65 1.1637797 -2.0402208 3.2656012 3.178054 3.16792675 -2.2072749 -3.649659
## X67 1.5303762 -1.3470736 3.1185934 2.639057 2.33213462 -2.4889147 -3.772261
## X68 1.7643559 -1.1394343 6.0996440 2.079442 3.55120786 -2.5510465 -3.540459
## X69 1.1637797 -2.2072749 2.9685108 2.564949 2.33213462 -4.2686979 -4.342806
## X70 2.0219013 -1.6607312 2.9685108 2.995732 3.26560115 -1.8971200 -3.324236
## X71 1.3273591 -2.1202635 4.3519974 2.772589 3.78147319 -2.0402208 -4.135167
## X72 2.2591348 -1.7147984 5.3589486 3.737670 4.04950827 -1.6607312 -3.506558
## X73 1.1637797 -1.6094379 5.3589486 3.178054 4.84829388 -1.3470736 -3.324236
## X74 2.5152196 -2.3751558 3.8717775 2.772589 3.26560115 -2.8134107 -2.995732
## X75 1.8088944 -2.3330443 6.4527639 3.401197 3.16792675 -1.8971200 -3.015935
## X76 1.6731213 -1.5141277 3.4097438 3.044522 1.35792679 -1.4696760 -2.577022
## X77 1.5303762 -1.5606477 4.6034206 2.833213 2.33213462 -2.3126354 -3.473768
## X78 2.6191813 -1.6607312 5.7354768 3.044522 4.92856213 -1.8971200 -3.036554
## X80 1.8527528 -1.6094379 3.8717775 2.833213 3.69015975 -1.8325815 -3.411248
## X81 0.4005958 -1.8971200 3.4097438 2.397895 1.35792679 -3.0365543 -3.540459
## X82 2.0219013 -1.8971200 2.7632020 2.890372 3.26560115 -2.1202635 -3.473768
## X83 2.3713615 -1.8971200 6.4527639 2.397895 3.55120786 -1.9661129 -3.411248
## X84 2.1427912 -1.6607312 3.9611107 2.397895 3.21692675 -3.2441936 -3.575551
## X85 2.1427912 -1.7147984 3.2656012 2.564949 3.64411202 -2.1202635 -3.688879
## X86 2.1427912 -1.4696760 3.5512079 3.295837 1.86752075 -1.4271164 -3.123566
## X88 2.6867663 -1.2729657 5.3589486 2.995732 2.81511562 -2.2072749 -3.270169
## X90 2.1820549 -2.3025851 5.7354768 2.708050 1.55530341 -3.6496587 -4.509860
## X93 1.7643559 -1.8325815 3.8717775 2.116256 2.96851076 -2.4418472 -3.688879
## X94 3.1563503 -1.4271164 6.0996440 3.891820 3.21692675 -1.3470736 -2.645075
## X95 2.5152196 -2.1202635 3.5512079 3.044522 2.33213462 -2.8134107 -3.688879
## X96 2.0219013 -1.6094379 4.0495083 2.944439 3.26560115 -2.2072749 -3.816713
## X97 2.0219013 -2.0402208 2.7632020 2.639057 4.39438269 -2.1202635 -3.649659
## X98 1.5303762 -2.2072749 0.9345728 2.564949 3.26560115 -3.9120230 -4.342806
## X99 1.5303762 -2.1202635 5.3589486 2.944439 2.86663136 -1.3470736 -3.963316
## X100 1.5303762 -1.8971200 3.1185934 2.054124 2.81511562 -2.0402208 -3.649659
## X103 1.1637797 -1.8325815 3.6901597 2.944439 3.78147319 -1.2729657 -2.995732
## X104 2.5502306 -1.5606477 4.2666237 3.295837 1.92776515 -2.2072749 -3.963316
## X105 1.1637797 -1.8971200 3.2656012 2.833213 3.16792675 -2.6310892 -3.863233
## X107 1.8527528 -1.6607312 4.0495083 2.772589 3.50434016 -2.5770219 -3.575551
## X108 2.1030230 -2.0402208 2.9685108 2.833213 2.81511562 -2.8647040 -3.912023
## X109 0.4005958 -2.4304185 3.2656012 2.833213 2.49726361 -3.3524072 -4.667046
## X110 1.6731213 -2.2072749 3.5512079 3.465736 1.22143685 -3.4420194 -3.963316
## X111 1.7643559 -1.4271164 3.2656012 3.044522 3.82674905 -2.1202635 -3.575551
## X112 2.3343863 -1.5606477 5.7354768 2.944439 2.81511562 -1.7147984 -2.631089
## X113 2.0219013 -1.9661129 4.9684528 3.135494 1.80658378 -2.3126354 -2.995732
## X114 2.0219013 -2.1202635 3.4097438 2.484907 2.55134197 -3.0791139 -4.199705
## X115 2.1427912 -1.5606477 4.0933668 2.397895 1.86752075 -2.6310892 -3.381395
## X117 1.5303762 -1.8971200 2.5513420 2.772589 2.55134197 -1.7719568 -3.575551
## X118 2.0219013 -1.1086626 3.9165632 3.091042 3.87177749 -0.5276327 -2.207275
## X121 2.3713615 -1.6607312 4.4785663 3.091042 1.61931977 -1.5606477 -3.575551
## X123 2.6867663 -2.5510465 2.7632020 2.397895 1.80658378 -3.3813948 -4.199705
## X124 1.7643559 -1.7719568 3.5043402 2.833213 3.55120786 -1.8971200 -3.863233
## X126 1.5303762 -1.8971200 3.1185934 2.272126 1.35792679 -1.9661129 -4.268698
## X128 2.5848812 -1.8971200 3.1185934 2.833213 2.10460236 -2.3644605 -3.272534
## X129 0.4005958 -2.1202635 3.2656012 2.397895 0.78296569 -3.1235656 -4.017384
## X130 2.0219013 -1.9661129 4.3519974 3.737670 3.87177749 -1.8971200 -4.017384
## X131 0.4005958 -2.3126354 3.6901597 2.397895 3.16792675 -2.9565116 -3.863233
## X132 1.5303762 -2.2072749 4.0495083 2.639057 2.33213462 -1.8971200 -3.506558
## X133 2.3343863 -1.8325815 4.3519974 2.708050 1.80658378 -2.4769385 -3.912023
## X134 1.6263611 -1.0216512 5.3589486 3.178054 3.26560115 -1.3862944 -2.813411
## X135 1.8527528 -1.9661129 2.3321346 2.564949 2.33213462 -3.6496587 -4.017384
## X136 2.9135187 -2.0402208 4.2666237 2.397895 3.55120786 -2.3434071 -3.575551
## X137 0.4005958 -2.2072749 3.6901597 2.104134 3.16792675 -2.7806209 -3.963316
## X139 2.0219013 -1.7719568 4.0495083 2.890372 3.26560115 -2.4769385 -3.442019
## X140 1.6263611 -1.4271164 3.2169268 2.564949 2.55134197 -2.5639499 -3.729701
## X141 1.0483341 -1.8325815 3.4097438 2.564949 4.39438269 -2.3025851 -4.074542
## X143 2.6867663 -2.1202635 4.9684528 3.332205 3.69015975 -2.4889147 -3.688879
## X144 2.0219013 -2.0402208 4.1804231 3.091042 1.80658378 -3.0791139 -3.912023
## X145 1.6731213 -2.3025851 2.3321346 2.833213 1.22143685 -3.1700857 -4.074542
## X146 2.3343863 -1.6094379 4.6034206 2.397895 2.55134197 -1.6094379 -2.975930
## X147 2.8501989 -1.2378744 4.6034206 3.295837 2.55134197 -1.6607312 -3.270169
## X148 2.9757467 -1.8325815 5.7354768 3.332205 2.96851076 -2.1202635 -3.411248
## X149 1.5303762 -2.2072749 4.0495083 3.044522 2.49726361 -2.5133061 -3.688879
## X152 2.0219013 -0.8439701 4.1804231 2.272126 2.55134197 -1.6607312 -3.244194
## X153 2.6191813 -1.7147984 4.4785663 3.178054 3.87177749 -2.3859667 -3.473768
## X154 0.4005958 -2.1202635 3.0689186 2.151762 2.49726361 -2.9565116 -4.422849
## X155 2.3343863 -1.8971200 1.7449255 2.944439 1.22143685 -3.0791139 -3.963316
## X156 0.4005958 -2.3126354 4.9684528 2.772589 4.84829388 -2.6310892 -3.506558
## X157 1.1637797 -1.9661129 3.6901597 3.258097 2.33213462 -2.5770219 -3.912023
## X158 1.1637797 -2.0402208 3.4571875 2.772589 3.16792675 -2.2072749 -3.575551
## X159 3.0064666 -2.1202635 3.6901597 2.708050 2.33213462 -2.6592600 -4.074542
## X160 1.1637797 -1.8971200 3.2656012 2.708050 4.84829388 -1.7147984 -3.540459
## X161 2.3713615 -1.7147984 3.9611107 2.833213 2.10460236 -2.3644605 -3.963316
## X162 2.1820549 -1.2378744 4.7673608 3.135494 3.55120786 -2.9004221 -4.074542
## X163 1.5303762 -2.0402208 3.0188940 2.708050 2.33213462 -3.6496587 -4.509860
## X165 2.6191813 -1.6607312 3.7359451 2.772589 2.55134197 -2.7333680 -3.473768
## X166 1.5303762 -1.7719568 1.9277652 3.044522 0.78296569 -2.8134107 -4.074542
## X167 2.1820549 -1.4696760 4.9285621 2.995732 4.09336677 -1.6607312 -2.864704
## X168 1.5303762 -2.3025851 4.0495083 2.833213 2.49726361 -2.3644605 -3.772261
## X169 2.0219013 -2.1202635 4.0495083 2.639057 3.87177749 -2.6172958 -4.342806
## X170 2.6191813 -1.4696760 4.0495083 2.484907 2.55134197 -3.8167128 -4.933674
## X171 1.0483341 -1.8325815 3.8267490 2.397895 2.33213462 -1.8971200 -3.270169
## X172 1.1637797 -2.2072749 3.5512079 3.178054 2.33213462 -2.7488722 -4.074542
## X174 2.7530556 -1.7147984 4.4785663 3.178054 3.21692675 -1.7719568 -3.101093
## X175 2.6191813 -1.8325815 4.3519974 1.974081 2.21947889 -2.1202635 -2.343407
## X176 1.5303762 -1.7147984 4.5619880 2.833213 3.01889401 -2.8647040 -3.912023
## X177 1.7643559 -1.8325815 4.6034206 3.258097 3.82674905 -1.6607312 -3.473768
## X178 1.9805094 -1.9661129 2.7632020 2.484907 1.86752075 -2.7181005 -3.688879
## X179 2.0219013 -2.3751558 4.9684528 2.484907 4.39438269 -2.7333680 -2.659260
## X180 2.8501989 -1.8971200 5.7354768 3.178054 1.80658378 -2.3434071 -3.649659
## X181 1.7643559 -2.1202635 5.3589486 2.639057 2.96851076 -3.0365543 -4.135167
## X182 0.4005958 -1.8971200 3.1679268 2.890372 2.33213462 -2.2072749 -3.912023
## X183 2.1030230 -2.5133061 2.3321346 2.833213 1.22143685 -2.7488722 -3.611918
## X184 2.0219013 -1.3093333 4.6034206 3.135494 3.26560115 -2.4769385 -3.473768
## X185 1.0483341 -2.0402208 4.5619880 2.639057 2.10460236 -2.5133061 -3.324236
## X186 1.6263611 -1.8325815 3.9165632 2.708050 2.55134197 -2.6172958 -3.688879
## X189 1.0483341 -1.6607312 3.7359451 3.218876 4.92856213 -2.6736488 -3.772261
## X190 1.2195081 -1.6607312 5.3589486 2.208274 2.96851076 -1.5606477 -3.912023
## X191 1.7643559 -1.7147984 4.6034206 2.890372 2.65813701 -2.2072749 -3.101093
## X192 2.1820549 -1.4271164 4.2666237 2.708050 3.55120786 -2.7968814 -3.473768
## X193 0.4005958 -1.4696760 2.3876751 2.564949 0.78296569 -2.3644605 -3.473768
## X194 1.7643559 -2.0402208 3.5043402 2.163323 2.33213462 -3.0791139 -3.688879
## X195 2.0219013 -2.0402208 2.8151156 3.496508 3.78147319 -1.5606477 -3.473768
## X197 1.6263611 -0.9416085 4.1804231 3.044522 4.13700376 -2.3025851 -3.575551
## X198 1.9805094 -2.1202635 3.2656012 2.302585 0.62482405 -2.7646206 -4.135167
## X200 1.5303762 -1.6607312 2.3321346 2.484907 2.81511562 -2.6310892 -3.575551
## X201 1.9805094 -1.8325815 5.7354768 2.772589 2.33213462 -2.3434071 -3.540459
## X202 1.5303762 -2.2072749 2.7632020 2.639057 2.55134197 -3.2968374 -4.074542
## X205 1.8088944 -1.8971200 4.6857433 2.890372 3.78147319 -1.8971200 -3.411248
## X208 2.6867663 -2.0402208 4.9285621 2.772589 2.33213462 -2.9957323 -3.816713
## X210 4.0237466 -2.2072749 4.1804231 2.639057 2.55134197 -2.1202635 -3.170086
## X212 1.0483341 -1.8325815 2.3876751 2.054124 2.33213462 -2.4889147 -4.342806
## X213 1.6263611 -1.9661129 4.6034206 2.397895 2.55134197 -1.4696760 -3.101093
## X214 2.0219013 -2.0402208 4.8079117 2.772589 3.78147319 -2.9565116 -3.218876
## X215 2.1030230 -1.9661129 3.5512079 2.995732 2.33213462 -1.8971200 -3.912023
## X216 4.0237466 -2.1202635 5.7354768 2.944439 3.55120786 -2.7030627 -3.473768
## X218 2.1820549 -1.9661129 5.7354768 2.484907 2.65813701 -2.5010360 -3.381395
## X219 2.1427912 -1.8971200 3.1185934 2.639057 2.81511562 -2.9374634 -4.199705
## X220 1.0483341 -1.8325815 3.1185934 2.890372 1.86752075 -2.5902672 -3.816713
## X223 1.6731213 -2.1202635 4.1804231 3.044522 2.33213462 -2.6310892 -3.575551
## X224 1.8527528 -2.0402208 3.5512079 2.772589 3.26560115 -1.8971200 -3.912023
## X225 1.5303762 -1.8971200 2.5513420 3.178054 3.26560115 -2.5770219 -4.135167
## X226 1.9805094 -1.7147984 5.7354768 2.772589 4.80791166 -1.1394343 -3.575551
## X227 2.3343863 -2.3859667 3.8717775 2.890372 3.26560115 -1.8325815 -3.649659
## X228 1.9805094 -2.3025851 2.5513420 2.186051 1.22143685 -2.6310892 -3.963316
## X229 1.0483341 -2.0402208 3.4097438 2.564949 2.21947889 -2.4534080 -3.270169
## X230 2.1820549 -1.2729657 5.3589486 2.995732 3.26560115 -2.2072749 -3.123566
## X231 2.6191813 -1.9661129 3.7359451 2.397895 3.87177749 -3.3524072 -3.912023
## X232 2.6867663 -1.7719568 4.2666237 2.639057 3.55120786 -2.5510465 -4.135167
## X233 2.9135187 -2.1202635 4.2666237 3.496508 2.33213462 -2.6450754 -3.688879
## X234 1.0483341 -1.8971200 2.4972636 2.944439 3.26560115 -2.1202635 -3.352407
## X236 2.0219013 -2.0402208 3.2169268 2.772589 4.39438269 -2.5639499 -3.575551
## X237 1.5303762 -1.8971200 3.1185934 2.833213 1.86752075 -2.3644605 -3.688879
## X239 1.8527528 -2.3859667 4.6034206 2.890372 1.80658378 -3.3813948 -4.199705
## X240 1.5303762 -2.1202635 3.1679268 2.944439 2.55134197 -2.6310892 -4.017384
## X241 1.6263611 -1.6607312 2.7632020 3.178054 4.39438269 -2.3126354 -3.611918
## X242 1.5303762 -2.6736488 4.0495083 2.944439 1.68251524 -2.7030627 -3.506558
## X243 0.4005958 -2.0402208 3.6901597 2.484907 0.78296569 -3.2441936 -3.772261
## X244 2.3713615 -1.7147984 3.2656012 2.639057 1.86752075 -2.1202635 -3.079114
## X245 1.9805094 -2.3968958 5.3589486 3.044522 2.33213462 -3.4737681 -4.342806
## X246 3.1563503 -2.3330443 4.7266389 2.772589 1.80658378 -2.6310892 -3.324236
## X247 2.1820549 -1.8325815 4.2666237 2.708050 2.96851076 -3.4737681 -3.863233
## X249 1.0483341 -1.4696760 3.7359451 2.772589 5.35894856 -2.2072749 -3.863233
## X250 1.8088944 -2.3025851 2.8151156 3.178054 3.16792675 -2.6310892 -3.611918
## X251 1.8527528 -1.8325815 3.1185934 2.772589 2.33213462 -3.1700857 -4.017384
## X253 1.5303762 -1.8325815 4.9684528 3.218876 3.21692675 -2.9374634 -3.863233
## X254 1.8527528 -1.8325815 5.3589486 2.833213 3.69015975 -1.6094379 -3.036554
## X255 1.0483341 -1.4271164 2.3876751 2.944439 2.81511562 -2.1202635 -3.270169
## X256 0.4005958 -1.7719568 2.3876751 2.302585 1.35792679 -3.0365543 -4.342806
## X257 1.0483341 -2.3330443 3.1185934 2.639057 1.35792679 -2.4079456 -3.863233
## X258 2.5152196 -1.8971200 5.7354768 3.688879 3.78147319 -2.4191189 -3.270169
## X260 2.3713615 -1.7719568 4.9285621 3.367296 3.55120786 -2.1202635 -3.611918
## X261 1.8527528 -2.3751558 3.4097438 2.151762 1.86752075 -3.2968374 -4.342806
## X262 2.5502306 -1.6094379 2.4972636 2.564949 4.60342060 -2.5510465 -4.422849
## X263 1.9805094 -1.2378744 3.2656012 2.890372 3.55120786 -2.2072749 -3.772261
## X264 2.5848812 -2.0402208 3.8267490 2.484907 2.81511562 -2.0402208 -3.442019
## X265 1.2195081 -1.9661129 3.0188940 2.484907 2.33213462 -2.3126354 -3.863233
## X267 2.0219013 -1.4696760 4.8482939 3.044522 3.87177749 -2.2072749 -3.816713
## X268 1.9805094 -1.2729657 4.2666237 2.944439 2.96851076 -2.5510465 -3.296837
## X269 1.5303762 -2.2072749 4.1804231 2.772589 2.33213462 -3.1700857 -4.342806
## X270 2.3713615 -1.5606477 4.9285621 3.610918 2.96851076 -2.5639499 -4.135167
## X271 1.9805094 -1.5606477 5.7354768 3.258097 3.55120786 -2.3227878 -3.352407
## X272 2.1820549 -1.5141277 4.4365713 2.995732 2.96851076 -2.2072749 -4.605170
## X273 2.3713615 -1.3862944 4.7673608 2.890372 2.33213462 -2.5902672 -3.506558
## X274 1.9805094 -1.6094379 4.0933668 2.397895 2.96851076 -2.1202635 -3.963316
## X275 1.8527528 -1.7719568 3.1679268 3.044522 2.81511562 -1.9661129 -3.540459
## X277 2.0219013 -1.9661129 4.6034206 2.397895 3.87177749 -2.3025851 -3.611918
## X278 2.1030230 -2.2072749 4.4785663 2.944439 2.81511562 -2.3025851 -3.772261
## X279 1.6263611 -1.8971200 3.4097438 2.302585 2.55134197 -4.4228486 -4.017384
## X281 2.7200688 -1.7719568 4.7266389 3.135494 1.80658378 -2.7030627 -3.170086
## X282 1.1637797 -1.8971200 4.3519974 2.772589 3.16792675 -2.7806209 -3.194183
## X283 3.3286939 -1.5545112 4.6034206 3.526361 3.55120786 -2.0402208 -3.473768
## X287 1.1637797 -2.1202635 3.6901597 2.772589 3.78147319 -2.4191189 -3.218876
## X289 2.1030230 -2.5383074 3.5512079 2.302585 1.49042759 -3.2968374 -4.342806
## X290 2.2591348 -2.1202635 4.9684528 3.610918 3.16792675 -3.0159350 -3.649659
## X291 1.6263611 -1.4271164 4.6034206 2.833213 2.55134197 -0.9416085 -2.830218
## X292 1.8527528 -1.8325815 4.9684528 3.178054 3.69015975 -1.8325815 -3.381395
## X294 1.8527528 -2.4769385 4.6034206 3.610918 1.80658378 -3.2968374 -3.963316
## X297 2.1427912 -2.0402208 2.3876751 2.484907 2.81511562 -2.4889147 -4.199705
## X298 0.4005958 -2.5010360 4.3519974 2.564949 3.16792675 -2.6310892 -3.963316
## X299 2.0219013 -1.4271164 3.7359451 2.995732 3.87177749 -2.9374634 -3.772261
## X301 1.8527528 -1.8325815 2.1623278 2.639057 0.09808809 -2.7181005 -4.268698
## X302 2.1030230 -2.8473123 4.0495083 2.772589 2.81511562 -2.7488722 -3.863233
## X303 1.7643559 -1.9661129 5.3589486 2.397895 3.55120786 -2.7333680 -4.268698
## X304 1.5303762 -1.6094379 3.6901597 2.944439 1.86752075 -1.9661129 -3.079114
## X305 1.7643559 -1.3862944 5.3589486 2.484907 3.55120786 -2.1202635 -3.540459
## X306 1.8527528 -1.7147984 6.0996440 3.465736 3.26560115 -1.6094379 -3.729701
## X307 2.3343863 -1.8325815 5.7354768 3.401197 3.26560115 -2.2072749 -3.324236
## X308 1.8527528 -1.7719568 4.1804231 2.140066 2.81511562 -2.3227878 -3.816713
## X311 1.8527528 -2.1202635 3.6901597 2.564949 2.33213462 -3.0365543 -3.649659
## X312 1.8527528 -1.6607312 4.3519974 3.295837 4.00542427 -1.9661129 -3.296837
## X313 1.5303762 -1.7719568 3.5043402 2.772589 0.62482405 -2.8647040 -4.074542
## X314 1.5303762 -1.5141277 2.4972636 2.282382 3.26560115 -2.7333680 -3.863233
## X315 1.5303762 -1.9661129 4.1804231 3.367296 2.81511562 -3.4420194 -3.688879
## X316 0.4005958 -2.0402208 3.0689186 2.397895 4.30941221 -3.0791139 -3.688879
## X317 1.8088944 -2.2072749 4.2236285 2.944439 3.16792675 -2.2072749 -3.381395
## X320 1.1637797 -2.3025851 4.6857433 2.484907 2.49726361 -2.2072749 -3.381395
## X321 0.4005958 -2.6310892 3.6901597 3.401197 2.49726361 -3.1941832 -4.199705
## X322 1.5303762 -1.8971200 4.6857433 2.639057 4.00542427 -2.2072749 -3.649659
## X323 1.9805094 -1.3862944 5.3589486 2.772589 3.55120786 -2.0402208 -3.540459
## X324 1.8527528 -1.6607312 3.5512079 2.833213 2.33213462 -2.6882476 -3.963316
## X325 1.8959582 -1.7719568 3.8717775 2.944439 2.33213462 -2.3025851 -3.352407
## X326 2.1030230 -1.9661129 5.3589486 2.890372 2.96851076 -3.0791139 -3.352407
## X327 2.3343863 -1.1086626 5.3589486 2.484907 3.55120786 -1.6094379 -3.381395
## X329 2.1427912 -1.8971200 3.4097438 3.044522 3.21692675 -2.1202635 -3.506558
## X330 2.3343863 -2.5010360 4.4785663 2.708050 3.87177749 -2.4304185 -3.352407
## X331 1.8959582 -1.6607312 3.2656012 2.028148 0.62482405 -2.9957323 -3.912023
## X332 1.7643559 -1.2729657 4.2666237 2.116256 1.55530341 -2.5510465 -3.816713
## X333 2.6867663 -1.3093333 4.9285621 3.044522 4.09336677 -1.8971200 -3.772261
## MMP7 Myoglobin NT_proBNP NrCAM Osteopontin PAI_1
## X1 -3.7735027 -1.89711998 4.553877 5.003946 5.356586 1.00350156
## X2 -5.9681907 -0.75502258 4.219508 5.209486 6.003887 -0.03059880
## X3 -4.0302269 -1.38629436 4.248495 4.744932 5.017280 0.43837211
## X5 -0.2222222 -1.77195684 4.465908 5.198497 5.693732 0.25230466
## X6 -1.9223227 -1.13943428 4.189655 3.258097 4.736198 0.43837211
## X7 -5.9681907 -1.77195684 4.330733 4.521789 5.318120 0.00000000
## X8 -2.4721360 -1.20397280 3.828641 3.258097 4.983607 0.49054798
## X9 -5.8446454 -1.96611286 5.043425 3.912023 5.049856 -0.47754210
## X11 -3.7735027 -1.66073121 4.875197 4.488636 5.533389 0.25230466
## X12 -3.0000000 -1.42711636 4.727388 3.988984 5.099866 0.25230466
## X14 -1.3806170 -1.60943791 4.691348 4.174387 5.023881 0.32004747
## X16 -4.0302269 -2.55104645 5.323010 4.812184 5.690359 0.49054798
## X17 -2.8507125 -1.17118298 4.595120 3.761200 5.043425 0.32004747
## X18 -1.2879797 -2.35387839 3.931826 3.637586 4.927254 0.32004747
## X19 -3.3452248 -0.82098055 4.290459 3.044522 4.804021 0.53887915
## X20 -0.6037782 -0.03045921 3.784190 3.970292 4.969813 0.85893499
## X21 -3.3452248 -1.56064775 5.262690 3.828641 4.997212 -0.65480247
## X22 -4.0302269 -2.71810054 4.828314 5.429346 6.308098 -0.15428707
## X23 -6.3770782 -2.53830743 3.663562 4.382027 5.351858 -0.04107298
## X24 -4.3245553 0.74193734 4.709530 4.934474 5.743003 -0.21752413
## X25 -4.0302269 -2.12026354 4.672829 3.988984 4.653960 -0.72247798
## X26 -3.5470020 0.87546874 4.499810 4.836282 5.568345 0.09396047
## X28 -4.0302269 -0.02020271 4.465908 4.204693 5.609472 -0.05168998
## X29 -2.2640143 -1.42711636 3.931826 3.295837 4.615121 -0.87443088
## X30 -3.7735027 -2.04022083 4.317488 4.158883 5.087596 -0.14221210
## X31 -3.3452248 -1.42711636 4.828314 3.931826 5.236442 0.09396047
## X34 -2.8507125 -1.13943428 4.770685 3.526361 4.919981 0.58384004
## X35 -2.5883147 -0.73396918 4.605170 3.637586 4.744932 0.00000000
## X36 -0.7216553 -1.46967597 4.718499 4.060443 4.812184 0.00000000
## X37 -3.7735027 1.68639895 4.595120 4.836282 5.826000 0.09396047
## X38 -4.7040152 -2.04022083 4.605170 4.248495 4.976734 0.25230466
## X39 -4.5938047 -1.83258146 4.262680 5.129899 5.529429 0.09396047
## X40 -1.6514837 -1.77195684 4.499810 4.356709 4.890349 0.32004747
## X41 -3.7735027 -1.34707365 4.983607 3.806662 5.081404 0.25230466
## X42 -4.3245553 -1.46967597 4.700480 4.477337 5.262690 -0.11859478
## X43 -3.1639778 -2.71810054 4.304065 3.871201 5.323010 -0.28605071
## X44 -4.4888568 -1.17118298 4.736198 4.369448 5.147494 0.62582535
## X45 -2.1702883 0.18232156 4.634729 4.543295 5.192957 0.17742506
## X46 -1.1622777 -1.20397280 4.499810 5.010635 5.529429 0.17742506
## X47 -2.8507125 -1.96611286 4.976734 3.912023 4.727388 -0.11859478
## X48 -4.0302269 -1.89711998 4.919981 5.332719 5.765191 0.49054798
## X50 -3.5470020 -1.60943791 5.129899 4.532599 5.214936 0.17742506
## X51 -4.6666667 -1.96611286 4.795791 4.753590 5.416100 -0.40885871
## X53 -3.7735027 -1.60943791 4.127134 4.442651 5.283204 0.09396047
## X55 -6.6874449 -1.77195684 4.127134 4.836282 4.882802 0.09396047
## X56 -4.3245553 -0.59783700 5.062595 4.143135 5.017280 0.17742506
## X57 -2.8507125 -1.07880966 4.574711 4.709530 5.323010 0.49054798
## X59 -3.0000000 -1.71479843 5.036953 4.290459 5.062595 1.10005082
## X60 -4.3887656 -1.60943791 4.736198 3.912023 5.062595 -0.27188464
## X61 -3.5470020 -2.47693848 4.488636 3.713572 5.023881 -0.25795574
## X62 -6.7705802 -2.70306266 4.574711 3.713572 4.653960 -0.55204550
## X63 -3.7735027 -1.77195684 4.948760 4.465908 5.493061 -0.01006550
## X64 -1.0151134 -1.71479843 5.181784 3.526361 5.318120 0.76993928
## X65 -4.0302269 -1.60943791 4.143135 4.418841 5.117994 0.09396047
## X67 -6.3045480 -2.20727491 4.859812 4.897840 5.771441 -0.16654597
## X68 -4.3245553 -0.15082289 3.610918 4.564348 5.521461 -0.04107298
## X69 -5.7849894 -1.38629436 4.304065 4.094345 4.718499 -0.11859478
## X70 -3.3452248 -2.04022083 5.003946 5.023881 5.105945 0.17742506
## X71 -4.0302269 -1.13943428 4.605170 4.430817 4.941642 0.73700033
## X72 -4.0302269 -1.20397280 4.634729 5.472271 5.549076 0.09396047
## X73 -5.2074997 -0.67334455 4.795791 5.159055 5.605802 0.58384004
## X74 -2.0000000 -2.20727491 4.406719 3.258097 4.290459 0.49054798
## X75 -2.4721360 -0.82098055 4.820282 3.737670 5.288267 0.58384004
## X76 -2.7140452 -2.12026354 4.770685 4.955827 5.921578 0.73700033
## X77 -4.5582584 -1.13943428 4.727388 4.442651 5.501258 0.76993928
## X78 -3.1639778 -1.66073121 4.990433 4.969813 5.921578 0.83076041
## X80 -2.3643578 -2.34340709 4.770685 4.543295 5.068904 0.17742506
## X81 -3.7735027 -2.30258509 4.859812 4.356709 5.081404 -0.14221210
## X82 -4.0302269 -2.60369019 4.406719 4.174387 5.252273 0.17742506
## X83 -3.5470020 1.41098697 4.595120 4.077537 4.770685 -0.16654597
## X84 -3.3452248 -1.66073121 4.543295 4.007333 5.332719 0.32004747
## X85 -3.7735027 -1.30933332 5.468060 4.615121 5.442418 0.43837211
## X86 -2.7140452 -0.61618614 5.117994 4.248495 5.318120 0.25230466
## X88 -3.1639778 -1.10866262 4.727388 4.812184 5.365976 0.38177502
## X90 -8.3975049 -2.79688141 3.178054 2.708050 4.234107 -0.63330256
## X93 -3.5470020 -0.49429632 4.317488 3.828641 4.779123 0.38177502
## X94 -1.4299717 -0.56211892 5.886104 4.820282 5.780744 0.80114069
## X95 -2.7140452 -0.84397007 4.762174 3.970292 4.867534 0.17742506
## X96 -3.3452248 -0.82098055 4.521789 4.867534 5.384495 -0.23078200
## X97 -4.5938047 -3.01593498 4.543295 5.111988 5.288267 -0.05168998
## X98 -7.3250481 -2.97592965 4.477337 3.970292 5.087596 -0.51401261
## X99 -3.5935279 -1.51412773 4.290459 4.727388 5.840642 0.62582535
## X100 -5.1611487 -0.24846136 4.634729 5.030438 5.351858 0.25230466
## X103 -3.2335542 -1.60943791 4.663439 5.631212 5.407172 0.43837211
## X104 -4.8199434 -1.71479843 4.430817 4.795791 4.934474 -0.17899381
## X105 -5.5592895 -2.31263543 4.454347 4.962845 5.351858 -0.27188464
## X107 -1.3806170 -2.12026354 4.369448 4.812184 4.976734 0.00000000
## X108 -1.9223227 -1.27296568 4.682131 4.442651 5.438079 0.09396047
## X109 -7.5346259 -2.20727491 4.465908 4.127134 4.882802 -0.24425708
## X110 -4.3245553 -2.43041846 4.043051 3.713572 5.220356 -0.06245326
## X111 -1.2879797 -1.60943791 4.110874 5.075174 5.159055 -0.47754210
## X112 -3.3452248 -0.38566248 5.323010 5.003946 5.857933 0.17742506
## X113 -2.3643578 0.53062825 4.787492 4.204693 5.303305 0.70214496
## X114 -5.1156807 -2.56394986 4.543295 3.583519 4.595120 -0.24425708
## X115 -2.2640143 -0.86750057 4.700480 3.871201 4.912655 -0.13031621
## X117 -4.3564173 -1.96611286 4.812184 5.087596 5.605802 0.38177502
## X118 -2.3643578 -2.12026354 4.700480 5.030438 5.662960 0.83076041
## X121 -0.4253563 -1.10866262 5.062595 4.682131 5.863631 0.53887915
## X123 -2.0000000 -1.66073121 4.304065 3.610918 4.795791 -0.42552800
## X124 -3.1639778 -0.99425227 4.875197 4.143135 5.010635 0.00000000
## X126 -4.8199434 -2.48891467 4.912655 4.624973 4.983607 -0.19163579
## X128 -1.2025631 -2.31263543 4.962845 4.820282 5.488938 0.95939061
## X129 -5.9056942 -1.89711998 4.624973 3.871201 4.605170 -0.57168558
## X130 -3.7735027 -0.86750057 5.159055 4.564348 5.099866 0.25230466
## X131 -5.0710678 -2.76462055 4.025352 4.488636 5.129899 -0.34523643
## X132 -4.0302269 0.69314718 4.442651 4.369448 5.017280 -0.08443323
## X133 -2.4721360 -0.75502258 4.672829 4.219508 4.859812 0.09396047
## X134 -0.5000000 -1.34707365 4.727388 4.672829 5.602119 0.43837211
## X135 -4.3245553 -2.45340798 4.584967 5.105945 5.257495 0.00000000
## X136 -2.4721360 -0.77652879 4.727388 3.828641 4.110874 0.43837211
## X137 -4.6299354 -1.42711636 4.488636 4.204693 4.976734 -0.01006550
## X139 -3.3452248 -1.34707365 4.543295 3.931826 5.017280 -0.59176325
## X140 -4.0302269 -1.89711998 4.406719 4.465908 5.187386 0.00000000
## X141 -3.1639778 -1.56064775 4.820282 4.890349 5.509388 0.25230466
## X143 -3.5470020 0.33647224 4.574711 3.637586 4.867534 0.00000000
## X144 -3.7735027 -2.04022083 4.276666 4.077537 5.030438 0.49054798
## X145 -5.0272837 -2.46510402 4.276666 4.330733 5.187386 -0.63330256
## X146 -2.1702883 -0.59783700 4.406719 4.430817 4.836282 0.43837211
## X147 -3.7735027 -1.23787436 4.634729 4.867534 5.743003 0.43837211
## X148 -1.7139068 -0.44628710 4.543295 4.276666 5.537334 0.09396047
## X149 -1.7139068 -2.88240359 4.290459 4.382027 4.897840 -0.27188464
## X152 -2.0824829 -0.94160854 4.682131 5.298317 5.634790 0.88578467
## X153 -3.5470020 -0.06187540 4.672829 4.488636 5.323010 0.00000000
## X154 -5.9681907 -1.71479843 3.806662 4.330733 5.442418 -0.36070366
## X155 -5.2547625 -2.47693848 4.442651 4.406719 5.164786 -0.24425708
## X156 -1.1622777 -0.40047757 4.369448 4.653960 5.176150 0.43837211
## X157 -3.1639778 -2.12026354 4.770685 4.356709 5.568345 1.00350156
## X158 -5.1611487 -2.59026717 3.828641 4.634729 4.962845 -0.51401261
## X159 -4.3245553 -2.20727491 4.859812 4.174387 4.941642 0.17742506
## X160 -4.5582584 -1.51412773 4.454347 5.501258 5.564520 -0.07336643
## X161 -5.0710678 0.40546511 5.081404 4.955827 5.361292 -0.69936731
## X162 -3.7735027 -0.63487827 4.406719 4.890349 6.144186 -0.07336643
## X163 -6.6874449 -2.83021784 4.262680 4.143135 5.429346 -0.57168558
## X165 -3.5470020 -2.30258509 4.499810 4.672829 5.484797 0.25230466
## X166 -5.4023321 0.09531018 4.624973 4.499810 5.257495 -0.31512364
## X167 -1.5921060 0.18232156 4.605170 5.283204 5.783825 -0.42552800
## X168 -0.9814240 -2.31263543 4.025352 4.532599 4.962845 -0.42552800
## X169 -2.0000000 -0.38566248 4.465908 4.330733 5.164786 0.00000000
## X170 -2.0000000 -2.61729584 4.499810 3.610918 5.159055 -0.10704332
## X171 -4.9421013 -0.63487827 4.948760 4.700480 5.187386 -0.45985790
## X172 -5.2074997 -3.12356565 4.510860 4.553877 5.318120 -0.65480247
## X174 -1.5921060 1.41098697 4.595120 4.356709 5.176150 0.17742506
## X175 -2.5883147 -0.77652879 4.584967 4.709530 5.323010 0.58384004
## X176 -5.0710678 -2.32278780 5.003946 4.204693 4.934474 0.32004747
## X177 -4.7419986 -2.40794561 4.770685 4.584967 5.375278 0.25230466
## X178 -4.0302269 -3.07911388 4.682131 4.077537 4.820282 -0.82104815
## X179 -2.1884251 -1.07880966 4.983607 4.060443 4.442651 -0.01006550
## X180 -2.0000000 -1.23787436 4.727388 4.553877 5.568345 -0.10704332
## X181 -5.5592895 1.77495235 4.382027 3.555348 4.356709 0.17742506
## X182 -5.0710678 -1.38629436 4.553877 4.510860 5.187386 0.49054798
## X183 -3.7735027 -1.71479843 4.406719 2.833213 4.248495 0.17742506
## X184 -5.4023321 1.06471074 4.653960 4.727388 5.225747 0.38177502
## X185 -4.4549722 -1.42711636 4.948760 4.304065 5.278115 0.49054798
## X186 -5.0272837 -1.60943791 4.304065 4.234107 5.214936 -0.08443323
## X189 -6.0321933 -0.84397007 4.672829 4.867534 5.710427 0.09396047
## X190 -4.0302269 -0.63487827 4.174387 4.521789 5.509388 0.32004747
## X191 -6.8561489 -1.56064775 4.700480 4.158883 5.332719 -0.63330256
## X192 -4.3245553 0.26236426 4.382027 5.056246 5.899897 0.25230466
## X193 -3.5470020 -2.04022083 5.283204 4.543295 5.488938 1.00350156
## X194 -2.8507125 -1.07880966 4.787492 3.663562 4.927254 0.17742506
## X195 -1.1234752 -0.34249031 4.488636 6.011267 5.978886 0.43837211
## X197 -2.2640143 -0.07257069 5.204007 4.753590 5.313206 -0.25795574
## X198 -5.5058663 -2.90042209 4.077537 4.043051 5.288267 -0.82104815
## X200 -4.0302269 -1.34707365 4.812184 4.584967 5.370638 0.00000000
## X201 -3.3452248 -1.46967597 4.204693 3.850148 4.962845 -0.06245326
## X202 -6.6066297 -1.42711636 4.204693 3.912023 5.293305 -0.49558921
## X205 -2.0000000 -0.47803580 4.369448 4.859812 5.332719 0.32004747
## X208 -5.0710678 -1.83258146 4.382027 4.234107 5.379897 -0.27188464
## X210 -3.0000000 -0.21072103 5.241747 4.653960 5.278115 0.53887915
## X212 -4.0302269 -0.96758403 4.644391 3.850148 4.948760 -0.20447735
## X213 -3.0000000 -0.51082562 4.836282 4.488636 5.181784 -0.08443323
## X214 -3.3452248 -1.83258146 4.304065 4.584967 5.093750 1.16610855
## X215 -1.6514837 -2.37515579 4.430817 4.867534 5.676754 -0.39250510
## X216 -4.4549722 -1.46967597 3.663562 3.850148 5.187386 0.25230466
## X218 -2.2640143 -0.18632958 4.204693 3.555348 4.997212 0.66516665
## X219 -5.1611487 -1.71479843 4.787492 4.094345 5.214936 -0.15428707
## X220 -5.8446454 -2.86470401 4.897840 4.691348 5.384495 -0.65480247
## X223 -3.3452248 -0.84397007 4.828314 4.430817 5.583496 -0.51401261
## X224 -5.8446454 -1.66073121 4.304065 4.615121 5.293305 0.00000000
## X225 -5.5058663 -1.10866262 4.394449 5.347108 5.826000 0.17742506
## X226 -3.0000000 -0.89159812 4.442651 5.411646 5.693732 0.43837211
## X227 -3.0000000 -0.91629073 4.077537 4.770685 4.969813 0.00000000
## X228 -1.4299717 -2.81341072 4.553877 3.828641 5.429346 -0.63330256
## X229 -0.4077171 -1.07880966 4.248495 3.663562 4.990433 -0.21752413
## X230 -2.7140452 -1.13943428 4.691348 4.219508 4.499810 0.43837211
## X231 -4.4888568 1.75785792 4.595120 4.158883 4.948760 0.00000000
## X232 -5.3521462 -1.83258146 4.143135 4.043051 4.934474 0.00000000
## X233 -5.3029674 -2.04022083 4.043051 3.433987 5.030438 -0.24425708
## X234 -3.3452248 -1.02976542 4.521789 4.317488 4.890349 -0.61229604
## X236 -4.0302269 -3.17008566 4.382027 4.691348 5.488938 -0.65480247
## X237 -4.0302269 0.09531018 4.574711 4.317488 5.153292 -0.09565753
## X239 -4.0302269 -0.77652879 4.394449 3.044522 4.584967 0.09396047
## X240 -4.0302269 0.74193734 4.442651 3.912023 4.795791 -0.42552800
## X241 -3.7735027 -2.52572864 4.859812 4.919981 5.117994 0.00000000
## X242 -1.7796447 -1.10866262 3.828641 2.833213 4.330733 0.25230466
## X243 -4.6299354 -1.51412773 5.075174 3.526361 4.787492 0.49054798
## X244 -3.7735027 -0.27443685 5.225747 4.454347 5.003946 0.85893499
## X245 -6.4515425 -0.22314355 3.871201 3.044522 4.317488 -0.16654597
## X246 -2.7140452 0.26236426 4.510860 4.094345 5.105945 0.25230466
## X247 -4.7806350 -2.45340798 4.553877 3.850148 5.049856 -0.57168558
## X249 -5.4023321 -1.46553683 4.820282 5.545177 6.102559 0.00000000
## X250 -4.5582584 -2.04022083 4.234107 4.430817 5.093750 0.09396047
## X251 -4.6299354 -1.27296568 4.553877 4.143135 4.779123 -0.55204550
## X253 -2.7140452 -1.96611286 4.962845 4.143135 5.135798 0.58384004
## X254 -2.3643578 -1.60943791 5.411646 5.598422 5.739793 0.09396047
## X255 -2.2640143 -1.46967597 4.875197 4.682131 5.170484 0.09396047
## X256 -6.3770782 -1.20397280 4.859812 4.615121 5.262690 -0.59176325
## X257 -5.7266741 -2.04022083 4.543295 3.931826 4.700480 -0.37645673
## X258 -3.1639778 -0.91629073 4.553877 4.859812 5.549076 0.09396047
## X260 -6.6066297 -1.96611286 4.204693 4.510860 4.844187 0.09396047
## X261 -7.1287093 -2.20727491 4.406719 3.871201 4.948760 -0.07336643
## X262 -5.7266741 -2.33304430 4.077537 3.931826 4.672829 -0.53282641
## X263 -1.2879797 -2.20727491 3.871201 4.663439 5.645447 -0.15428707
## X264 -2.0824829 -0.22314355 5.707110 4.127134 5.267858 0.17742506
## X265 -6.0977633 -2.37515579 3.433987 4.356709 5.214936 0.09396047
## X267 -2.0000000 -1.07880966 4.882802 4.499810 5.739793 0.66516665
## X268 -3.5470020 -0.96758403 4.465908 4.700480 5.525453 -0.47754210
## X269 -5.5058663 -2.93746337 4.624973 4.077537 4.852030 -0.99084860
## X270 -1.8490018 -0.41551544 4.025352 5.030438 5.945421 -0.07336643
## X271 -2.2640143 -1.13943428 4.488636 4.234107 5.616771 -0.02026405
## X272 -4.4216130 -1.46967597 4.564348 4.127134 4.962845 -0.10704332
## X273 -3.0000000 -0.63487827 4.043051 4.532599 5.247024 0.32004747
## X274 -5.6696499 0.33647224 3.970292 4.488636 5.147494 -0.03059880
## X275 -4.0302269 -1.27296568 4.442651 4.867534 5.370638 0.17742506
## X277 -1.7139068 -1.04982212 4.143135 4.356709 5.010635 0.53887915
## X278 -3.1639778 -1.96611286 4.663439 4.394449 5.225747 -0.10704332
## X279 -3.1639778 -2.71810054 4.753590 4.025352 4.976734 -0.13031621
## X281 -3.3452248 -0.76744135 4.276666 4.691348 5.521461 0.70214496
## X282 -3.7735027 -0.93350237 3.828641 3.737670 4.644391 0.38177502
## X283 -4.0302269 -1.30933332 4.477337 5.147494 5.679253 0.43837211
## X287 -3.7735027 -1.66073121 4.454347 4.736198 5.886104 0.09396047
## X289 -5.3029674 -0.28768207 3.951244 3.663562 4.912655 -0.23078200
## X290 -3.5470020 -2.20727491 3.610918 3.828641 5.147494 0.09396047
## X291 -0.9814240 -1.77195684 4.356709 4.828314 5.472271 0.70214496
## X292 -4.0302269 -2.12026354 4.779123 5.003946 5.556828 0.32004747
## X294 -6.0977633 -1.13943428 4.553877 3.295837 5.257495 -0.55204550
## X297 -4.3887656 -1.77195684 4.564348 4.110874 4.663439 -0.16654597
## X298 -5.5058663 -2.04022083 3.951244 3.637586 4.532599 0.00000000
## X299 -5.1156807 -2.04022083 4.927254 4.488636 5.560682 0.09396047
## X301 -4.4549722 -2.32278780 4.356709 4.127134 4.875197 -0.17899381
## X302 -4.0302269 -2.04022083 4.343805 3.218876 4.343805 -0.63330256
## X303 -5.2547625 -1.83258146 4.442651 4.234107 5.288267 -0.20447735
## X304 -3.0000000 -2.35387839 5.111988 5.075174 6.304449 0.62582535
## X305 -3.7735027 -1.46967597 4.682131 4.094345 5.662960 0.73700033
## X306 -4.4216130 -0.77652879 4.595120 4.941642 5.267858 0.38177502
## X307 -3.5470020 -0.71334989 4.779123 4.962845 6.129050 0.85893499
## X308 -6.9442719 -0.84397007 4.595120 4.510860 5.135798 0.09396047
## X311 -3.7735027 -1.77195684 4.770685 4.043051 4.828314 -0.11859478
## X312 -3.7735027 -1.96611286 4.912655 5.075174 5.164786 0.32004747
## X313 -5.0710678 -0.35667494 4.317488 3.688879 5.288267 0.17742506
## X314 -6.5280287 -2.90042209 4.709530 4.406719 5.293305 -0.17899381
## X315 -3.7735027 -1.89711998 4.418841 4.007333 5.093750 -0.09565753
## X316 -5.1611487 -1.60943791 4.290459 4.736198 5.411646 0.25230466
## X317 -4.9421013 -2.12026354 4.653960 4.844187 5.081404 -0.04107298
## X320 -4.6299354 -1.23787436 4.465908 4.007333 4.304065 -0.28605071
## X321 -4.6666667 -1.46967597 3.784190 2.639057 4.454347 -0.10704332
## X322 -0.8867513 -0.77652879 4.912655 4.465908 5.393628 0.49054798
## X323 -3.5470020 -0.35667494 5.135798 4.382027 5.236442 0.32004747
## X324 -6.3045480 -1.13943428 4.875197 4.219508 5.170484 0.25230466
## X325 -4.3245553 -2.40794561 4.488636 4.442651 4.941642 0.85893499
## X326 -3.7735027 -0.38566248 4.510860 4.276666 5.288267 0.09396047
## X327 -0.7472113 0.33647224 4.890349 4.795791 5.236442 0.53887915
## X329 -4.9843030 -2.37515579 4.465908 5.192957 5.416100 0.17742506
## X330 -1.2025631 -0.52763274 4.744932 3.761200 4.488636 0.09396047
## X331 -6.1649658 -1.83258146 4.304065 3.931826 4.762174 -0.09565753
## X332 -3.7735027 -0.77652879 4.189655 3.465736 4.859812 0.17742506
## X333 -5.5058663 -0.63487827 4.465908 5.541264 6.102559 -0.53282641
## PAPP_A PLGF PYY Pancreatic_polypeptide Prolactin
## X1 -2.902226 4.442651 3.218876 0.57878085 0.00000000
## X2 -2.813276 4.025352 3.135494 0.33647224 -0.51082562
## X3 -2.935541 4.510860 2.890372 -0.89159812 -0.13926207
## X5 -2.935541 4.795791 3.663562 0.26236426 0.18232156
## X6 -2.935541 4.394449 3.332205 -0.47803580 -0.15082289
## X7 -2.590291 3.367296 2.833213 -0.59783700 -0.52763274
## X8 -2.669471 4.343805 2.397895 -0.31471074 -0.35667494
## X9 -2.786601 3.526361 2.833213 -0.52763274 0.40546511
## X11 -2.669471 4.356709 3.258097 -1.27296568 0.40546511
## X12 -2.971157 3.871201 2.995732 1.16315081 0.26236426
## X14 -2.841320 4.330733 2.772589 -0.37106368 0.09531018
## X16 -2.971157 4.189655 2.833213 0.33647224 -0.03045921
## X17 -2.691054 4.219508 2.639057 0.78845736 0.26236426
## X18 -2.841320 4.189655 2.833213 -0.59783700 -0.44628710
## X19 -2.713475 3.784190 2.833213 0.18232156 0.18232156
## X20 -2.813276 4.454347 2.302585 -0.26136476 0.26236426
## X21 -2.813276 4.007333 3.178054 0.69314718 0.18232156
## X22 -2.713475 3.258097 2.833213 -1.23787436 -0.22314355
## X23 -3.110843 4.262680 3.135494 -0.82098055 -0.21072103
## X24 -2.590291 3.465736 2.772589 -0.04082199 -0.05129329
## X25 -2.691054 3.433987 2.890372 -1.27296568 -1.30933332
## X26 -2.902226 3.688879 2.772589 0.09531018 0.18232156
## X28 -3.009464 3.713572 2.772589 0.40546511 -0.23572233
## X29 -2.736812 3.912023 2.995732 0.09531018 -0.10536052
## X30 -2.813276 3.583519 2.995732 0.09531018 -0.13926207
## X31 -2.590291 4.488636 3.218876 0.33647224 -0.10536052
## X34 -2.813276 4.727388 2.397895 0.91629073 -0.13926207
## X35 -3.050970 4.110874 2.833213 0.53062825 0.00000000
## X36 -2.736812 4.499810 2.833213 -0.75502258 0.09531018
## X37 -2.971157 3.912023 2.995732 -0.10536052 -0.03045921
## X38 -2.902226 3.871201 3.178054 -0.63487827 0.18232156
## X39 -2.841320 3.332205 3.367296 -0.71334989 -0.40047757
## X40 -2.761153 3.891820 3.218876 -0.51082562 -0.07230692
## X41 -2.813276 4.094345 2.833213 0.18232156 0.09531018
## X42 -2.902226 4.025352 3.367296 -1.27296568 0.47000363
## X43 -2.971157 3.806662 2.833213 0.00000000 0.40546511
## X44 -2.841320 4.532599 3.135494 0.47000363 0.40546511
## X45 -2.590291 4.143135 2.890372 0.64185389 0.00000000
## X46 -3.110843 4.382027 2.890372 -0.26136476 0.26236426
## X47 -2.971157 3.828641 2.995732 0.18232156 0.00000000
## X48 -2.691054 4.248495 3.496508 0.69314718 0.09531018
## X50 -2.713475 4.127134 3.850148 -0.41551544 0.18232156
## X51 -2.841320 3.295837 2.639057 -0.96758403 0.09531018
## X53 -2.902226 4.007333 3.332205 -0.34249031 0.09531018
## X55 -2.902226 3.871201 3.135494 0.26236426 -0.10536052
## X56 -2.841320 4.290459 2.833213 -0.46203546 -0.07257069
## X57 -2.813276 4.499810 3.295837 1.06471074 -0.13926207
## X59 -2.713475 4.663439 2.995732 -0.32850407 0.33647224
## X60 -2.736812 3.806662 2.833213 0.95551145 0.26236426
## X61 -2.761153 3.931826 3.433987 -0.09431068 -0.40047757
## X62 -3.009464 3.496508 2.708050 -0.73396918 -0.23572233
## X63 -2.628559 4.077537 3.135494 0.91629073 -0.30110509
## X64 -2.813276 4.343805 2.944439 0.83290912 -0.17435339
## X65 -2.971157 3.332205 2.890372 0.83290912 -0.18632958
## X67 -3.009464 3.850148 2.397895 -0.32850407 0.09531018
## X68 -2.691054 3.663562 2.995732 0.26236426 0.09531018
## X69 -2.935541 3.555348 2.995732 -0.16251893 -0.01005034
## X70 -2.935541 3.737670 3.091042 0.26236426 -0.31471074
## X71 -3.068611 4.369448 2.772589 0.40546511 -0.18632958
## X72 -2.813276 4.369448 2.890372 -0.59783700 0.58778666
## X73 -2.786601 4.615121 2.890372 0.26236426 0.99325177
## X74 -2.813276 3.871201 3.091042 0.53062825 -0.06187540
## X75 -2.935541 4.605170 2.833213 0.69314718 0.09531018
## X76 -2.813276 4.043051 3.135494 1.02961942 -0.26136476
## X77 -2.761153 3.761200 3.044522 0.83290912 0.69314718
## X78 -2.935541 4.418841 2.639057 1.52605630 0.18232156
## X80 -2.902226 3.891820 3.401197 0.33647224 0.09531018
## X81 -3.009464 4.189655 3.178054 -0.63487827 0.26236426
## X82 -2.761153 3.135494 2.995732 0.09531018 0.26236426
## X83 -2.971157 3.970292 3.178054 -0.40047757 0.18232156
## X84 -2.935541 3.871201 2.772589 -0.63487827 -0.02020271
## X85 -2.935541 3.761200 3.135494 -0.32850407 -0.17435339
## X86 -2.870902 4.317488 3.496508 1.93152141 -0.09431068
## X88 -2.669471 4.634729 3.465736 0.47000363 -0.06187540
## X90 -2.902226 3.555348 2.397895 -0.40047757 -0.75502258
## X93 -2.902226 3.761200 2.833213 0.18232156 -0.26136476
## X94 -2.902226 4.844187 3.218876 1.25276297 0.18232156
## X95 -2.736812 3.806662 3.367296 0.47000363 0.18232156
## X96 -3.068611 3.713572 3.332205 -0.79850770 -0.05129329
## X97 -2.971157 3.218876 2.772589 -0.67334455 -0.22314355
## X98 -2.870902 3.496508 2.995732 -1.02165125 0.33647224
## X99 -3.033945 4.465908 2.708050 0.91629073 0.53062825
## X100 -2.870902 3.828641 3.295837 -0.23572233 0.00000000
## X103 -2.841320 4.382027 2.564949 -0.19845094 0.00000000
## X104 -2.870902 3.891820 2.639057 0.26236426 -0.15082289
## X105 -2.648659 3.044522 3.044522 0.26236426 -0.19845094
## X107 -2.841320 3.332205 3.178054 -0.16251893 0.26236426
## X108 -2.590291 3.713572 2.833213 -0.03045921 -0.22314355
## X109 -3.110843 3.433987 3.044522 -0.71334989 0.18232156
## X110 -2.813276 3.713572 2.833213 -2.12026354 -0.30110509
## X111 -2.870902 3.784190 2.833213 -0.40047757 0.00000000
## X112 -2.713475 3.951244 2.995732 1.64865863 -0.21072103
## X113 -2.761153 4.595120 2.890372 0.69314718 0.09531018
## X114 -3.101129 3.526361 2.772589 -0.73396918 0.33647224
## X115 -2.648659 4.465908 2.708050 1.09861229 -0.16251893
## X117 -2.590291 3.784190 3.091042 0.18232156 -0.11653382
## X118 -2.648659 3.988984 3.583519 -1.27296568 0.09531018
## X121 -3.082278 4.330733 2.708050 0.18232156 0.26236426
## X123 -2.761153 3.526361 3.218876 -0.23572233 0.33647224
## X124 -2.609119 4.094345 3.258097 0.09531018 0.26236426
## X126 -2.786601 3.465736 2.995732 -0.52763274 0.09531018
## X128 -2.902226 3.828641 3.178054 0.47000363 0.26236426
## X129 -2.935541 3.433987 2.833213 -0.75502258 -0.07257069
## X130 -2.902226 4.143135 3.367296 0.58778666 0.40546511
## X131 -3.013461 2.995732 2.772589 0.78845736 0.00000000
## X132 -2.813276 3.806662 2.833213 -0.31471074 -0.23572233
## X133 -2.736812 4.204693 2.833213 -0.52763274 -0.08338161
## X134 -2.736812 4.204693 3.178054 1.25276297 0.00000000
## X135 -2.761153 3.637586 2.833213 -0.31471074 -0.27443685
## X136 -2.971157 4.094345 3.091042 0.69314718 0.91629073
## X137 -2.870902 4.094345 3.135494 -0.34249031 0.00000000
## X139 -2.870902 3.761200 3.526361 -0.47803580 0.26236426
## X140 -2.609119 3.828641 3.091042 1.19392247 0.33647224
## X141 -2.870902 3.332205 2.890372 0.18232156 0.09531018
## X143 -2.609119 4.317488 2.833213 0.99325177 -0.30110509
## X144 -2.902226 3.610918 3.178054 0.58778666 -0.04082199
## X145 -2.870902 3.295837 2.995732 -0.86750057 0.00000000
## X146 -2.813276 3.663562 2.186051 1.33500107 -0.09431068
## X147 -3.009464 3.496508 2.890372 0.78845736 -0.27443685
## X148 -2.870902 4.189655 2.639057 0.00000000 -0.18632958
## X149 -2.813276 3.761200 3.044522 -1.10866262 -0.12783337
## X152 -2.691054 4.488636 2.890372 1.13140211 -0.22314355
## X153 -2.648659 4.158883 2.995732 0.64185389 -0.07257069
## X154 -2.935541 3.496508 3.218876 -0.71334989 -0.02020271
## X155 -2.902226 3.784190 3.332205 -0.86750057 0.09531018
## X156 -2.870902 4.262680 3.044522 -0.26136476 0.18232156
## X157 -2.813276 4.304065 2.995732 0.26236426 0.33647224
## X158 -2.813276 4.077537 2.890372 -0.96758403 -0.01005034
## X159 -2.935541 4.043051 3.044522 0.18232156 -0.07257069
## X160 -2.902226 3.828641 3.332205 0.33647224 0.18232156
## X161 -2.870902 4.025352 3.135494 -1.27296568 0.18232156
## X162 -2.841320 3.367296 3.806662 -0.40047757 0.40546511
## X163 -2.935541 2.944439 3.465736 -0.96758403 -0.03045921
## X165 -2.971157 3.218876 2.890372 -1.07880966 -0.21072103
## X166 -2.841320 3.737670 2.944439 -0.23572233 -0.35667494
## X167 -2.691054 3.401197 2.944439 -1.34707365 0.00000000
## X168 -2.870902 3.610918 2.833213 -0.82098055 0.00000000
## X169 -3.017491 3.737670 3.178054 -0.47803580 0.18232156
## X170 -2.841320 3.737670 2.890372 -0.61618614 0.26236426
## X171 -2.786601 3.367296 3.526361 -0.09431068 -0.03045921
## X172 -2.813276 3.526361 3.218876 -0.16251893 0.53062825
## X174 -2.870902 4.624973 2.995732 0.83290912 0.53062825
## X175 -3.136040 3.850148 3.135494 0.33647224 -0.21072103
## X176 -2.971157 4.762174 2.708050 0.64185389 0.33647224
## X177 -2.841320 4.007333 2.397895 -0.16251893 -0.15082289
## X178 -2.971157 3.433987 2.995732 -0.99425227 0.18232156
## X179 -2.761153 4.077537 2.890372 -0.34249031 0.09531018
## X180 -2.971157 4.007333 2.833213 0.58194114 0.26236426
## X181 -2.902226 3.610918 2.833213 -0.47803580 -0.22314355
## X182 -2.713475 3.761200 3.091042 -0.09431068 0.00000000
## X183 -2.870902 4.077537 2.772589 -1.42711636 0.25361982
## X184 -2.669471 4.143135 2.639057 -0.31471074 0.09531018
## X185 -2.902226 4.007333 2.995732 0.78845736 -0.15082289
## X186 -3.033945 3.806662 2.944439 0.64185389 0.18232156
## X189 -2.971157 2.484907 2.890372 1.56861592 -0.09431068
## X190 -2.902226 3.583519 2.833213 -0.40047757 -0.06187540
## X191 -2.870902 4.077537 2.833213 -1.96611286 -0.67334455
## X192 -2.935541 3.295837 2.397895 1.30833282 -0.51082562
## X193 -2.813276 4.836282 2.708050 0.87546874 -0.16251893
## X194 -2.713475 3.713572 3.295837 -0.52763274 0.26236426
## X195 -3.009464 4.234107 3.135494 -0.46203546 0.00000000
## X197 -2.971157 3.713572 3.044522 -0.23572233 0.00000000
## X198 -2.609119 2.995732 3.295837 -1.23787436 0.18232156
## X200 -2.870902 3.496508 3.610918 -0.49429632 -0.16251893
## X201 -2.971157 4.127134 3.044522 -0.23572233 -0.46203546
## X202 -2.841320 3.091042 3.433987 -0.86750057 0.26236426
## X205 -2.736812 4.499810 3.218876 -0.41551544 -0.18632958
## X208 -2.935541 3.737670 2.397895 0.00000000 -0.27443685
## X210 -2.761153 4.394449 3.332205 0.47000363 0.18232156
## X212 -3.050970 3.850148 2.833213 1.93152141 0.40546511
## X213 -3.033945 3.737670 2.995732 1.19392247 0.33647224
## X214 -2.902226 4.219508 2.833213 0.87546874 -0.06187540
## X215 -2.935541 3.218876 3.367296 0.47000363 -0.28768207
## X216 -2.786601 4.189655 3.044522 -0.31471074 -0.01005034
## X218 -2.713475 4.234107 3.135494 -0.01005034 0.09531018
## X219 -2.971157 3.610918 3.295837 0.87546874 -0.40047757
## X220 -2.971157 3.970292 2.995732 -0.37106368 0.33647224
## X223 -2.935541 3.218876 3.091042 0.91629073 -0.12783337
## X224 -2.786601 3.496508 2.708050 -0.73396918 -0.41551544
## X225 -2.841320 3.806662 3.044522 -0.49429632 -0.46203546
## X226 -3.009464 3.401197 2.639057 0.18232156 0.00000000
## X227 -2.902226 4.127134 3.332205 1.19392247 -0.05129329
## X228 -2.935541 3.806662 2.833213 -0.31471074 -0.27443685
## X229 -3.009464 3.850148 3.178054 -0.73396918 -0.13926207
## X230 -3.179436 4.276666 2.890372 1.62924054 -0.05129329
## X231 -2.902226 4.330733 3.332205 -0.67334455 0.33647224
## X232 -2.736812 4.077537 3.218876 -0.40047757 -0.17435339
## X233 -2.813276 4.127134 2.639057 -0.63487827 0.18232156
## X234 -2.736812 3.828641 2.944439 0.74193734 -0.01005034
## X236 -3.009464 3.295837 3.178054 -0.94160854 0.09531018
## X237 -3.120760 4.060443 3.135494 1.09861229 -0.11653382
## X239 -2.971157 3.806662 3.091042 -0.16251893 0.09531018
## X240 -2.713475 3.784190 3.091042 0.18232156 -0.18632958
## X241 -2.935541 3.258097 2.708050 -0.31471074 0.58778666
## X242 -3.310880 4.208969 2.833213 0.99325177 0.09531018
## X243 -3.068611 4.219508 2.995732 -0.02020271 -0.54472718
## X244 -2.870902 4.060443 2.995732 0.74193734 0.53062825
## X245 -2.935541 3.850148 3.135494 -0.40047757 -0.47803580
## X246 -2.691054 3.988984 3.218876 0.58778666 0.09531018
## X247 -2.691054 3.828641 3.044522 -0.16251893 0.26236426
## X249 -2.736812 3.465736 2.484907 -1.07880966 0.18232156
## X250 -3.009464 4.110874 2.890372 -0.82098055 0.18232156
## X251 -2.971157 3.737670 2.944439 0.09531018 0.09531018
## X253 -3.120760 4.644391 3.218876 0.33647224 -0.26136476
## X254 -2.786601 4.025352 2.995732 -0.59783700 0.69314718
## X255 -2.870902 4.060443 3.367296 -0.63487827 0.33647224
## X256 -2.971157 3.367296 3.295837 0.53062825 -0.03045921
## X257 -2.870902 3.737670 3.688879 -0.09431068 0.47000363
## X258 -2.713475 4.317488 3.135494 0.33647224 0.09531018
## X260 -2.648659 3.713572 2.833213 -0.69314718 0.09531018
## X261 -3.136040 3.713572 3.295837 0.00000000 0.26236426
## X262 -2.669471 3.713572 3.044522 -0.69314718 -0.06187540
## X263 -2.870902 3.871201 3.135494 -1.13943428 -0.02020271
## X264 -2.813276 4.382027 2.833213 0.60449978 0.18232156
## X265 -2.786601 3.784190 2.639057 0.91629073 -0.09431068
## X267 -2.786601 4.234107 2.639057 0.83290912 0.09531018
## X268 -2.935541 3.555348 3.044522 0.40546511 0.26236426
## X269 -2.648659 3.737670 2.995732 -1.42711636 -0.08338161
## X270 -2.902226 3.737670 2.397895 -0.47803580 0.18232156
## X271 -2.609119 5.170484 3.433987 -0.23572233 0.00000000
## X272 -2.841320 3.218876 2.833213 -0.23572233 0.47000363
## X273 -2.609119 4.110874 3.295837 0.09531018 -0.43078292
## X274 -2.841320 3.555348 2.833213 -1.13943428 -0.49429632
## X275 -2.935541 3.433987 3.401197 -0.49429632 -0.40047757
## X277 -2.841320 4.110874 2.639057 0.53062825 0.33647224
## X278 -2.648659 3.871201 2.708050 0.18232156 0.09531018
## X279 -2.971157 3.091042 2.772589 -0.94160854 -0.01005034
## X281 -2.841320 4.499810 3.178054 0.18232156 -0.06187540
## X282 -3.068611 3.931826 2.890372 0.33647224 0.09531018
## X283 -2.554309 3.850148 3.135494 0.58778666 -0.13926207
## X287 -3.033945 3.610918 3.135494 0.26236426 0.18232156
## X289 -2.736812 4.158883 2.833213 -0.59783700 0.09531018
## X290 -2.870902 4.510860 3.091042 0.33647224 0.53062825
## X291 -2.935541 3.850148 3.044522 -0.10536052 0.18232156
## X292 -2.520328 3.610918 3.465736 -0.23572233 0.09531018
## X294 -2.971157 4.094345 2.708050 -1.02165125 -0.59783700
## X297 -2.971157 3.912023 2.995732 0.74193734 0.64185389
## X298 -2.713475 4.510860 2.772589 0.09531018 0.74193734
## X299 -2.691054 3.465736 3.332205 -0.94160854 0.91629073
## X301 -2.669471 3.828641 2.708050 -0.41551544 0.18232156
## X302 -2.648659 3.526361 3.091042 -0.86750057 -0.10536052
## X303 -2.628559 4.127134 2.639057 -0.16251893 -0.22314355
## X304 -2.841320 4.304065 3.367296 0.18232156 0.26236426
## X305 -2.669471 4.477337 3.135494 0.09531018 -0.06187540
## X306 -2.902226 4.219508 3.496508 0.18232156 0.26236426
## X307 -2.841320 4.110874 3.610918 0.99325177 -0.04082199
## X308 -2.786601 3.637586 3.295837 0.99325177 -0.02020271
## X311 -2.971157 4.174387 2.944439 -0.41551544 0.40546511
## X312 -3.196833 4.025352 3.931826 0.78845736 -0.43078292
## X313 -2.648659 4.127134 3.433987 0.00000000 0.18232156
## X314 -2.609119 3.555348 3.295837 -0.96758403 0.18232156
## X315 -2.935541 3.931826 2.995732 -0.16251893 0.91629073
## X316 -2.935541 3.784190 2.833213 0.09531018 0.09531018
## X317 -3.068611 3.295837 3.135494 -0.34249031 0.26236426
## X320 -2.786601 3.784190 3.761200 0.40546511 -0.11653382
## X321 -3.009464 4.043051 3.044522 0.26236426 0.40546511
## X322 -2.971157 4.454347 3.135494 -0.52763274 0.33647224
## X323 -2.813276 4.158883 3.367296 0.53062825 0.18232156
## X324 -3.009464 3.663562 3.044522 0.58778666 -0.04082199
## X325 -2.902226 4.234107 2.397895 0.78845736 0.64185389
## X326 -2.628559 3.135494 2.995732 -0.04082199 0.09531018
## X327 -3.033945 4.304065 2.639057 0.78845736 0.33647224
## X329 -3.157227 4.330733 2.708050 0.33647224 0.00000000
## X330 -2.902226 4.317488 3.178054 0.78845736 0.47000363
## X331 -3.091611 3.610918 3.218876 -0.96758403 0.47000363
## X332 -2.648659 4.276666 2.833213 -1.34707365 0.26236426
## X333 -2.609119 3.583519 2.944439 -0.52763274 -0.30110509
## Prostatic_Acid_Phosphatase Protein_S Pulmonary_and_Activation_Regulat
## X1 -1.620527 -1.784998 -0.8439701
## X2 -1.739232 -2.463991 -2.3025851
## X3 -1.636682 -2.259135 -1.6607312
## X5 -1.696685 -1.659842 -0.5621189
## X6 -1.755051 -2.357788 -1.1711830
## X7 -1.659412 -2.259135 -1.5606477
## X8 -1.724319 -2.081112 -1.1086626
## X9 -1.763357 -2.167156 -1.6607312
## X11 -1.690161 -2.081112 -1.2039728
## X12 -1.710172 -2.259135 -0.8439701
## X14 -1.548336 -2.081112 -1.0498221
## X16 -1.631218 -2.463991 -1.0216512
## X17 -1.677510 -2.000377 -1.0498221
## X18 -1.710172 -2.703458 -2.2072749
## X19 -1.636682 -2.357788 -0.5798185
## X20 -1.631218 -1.852753 -1.1711830
## X21 -1.724319 -2.259135 -1.1394343
## X22 -1.677510 -2.357788 -1.9661129
## X23 -1.671366 -2.578792 -2.0402208
## X24 -1.696685 -2.357788 -1.3862944
## X25 -1.755051 -2.578792 -2.1202635
## X26 -1.665336 -2.357788 -1.8971200
## X28 -1.677510 -2.000377 -1.7719568
## X29 -1.677510 -2.839536 -1.8325815
## X30 -1.615292 -2.703458 -1.8971200
## X31 -1.665336 -1.852753 -1.6094379
## X34 -1.659412 -2.167156 -1.2039728
## X35 -1.671366 -2.000377 -0.9942523
## X36 -1.659412 -1.720797 -0.7985077
## X37 -1.620527 -2.463991 -1.8971200
## X38 -1.696685 -2.167156 -1.5141277
## X39 -1.671366 -2.463991 -2.2072749
## X40 -1.647864 -2.167156 -2.1202635
## X41 -1.677510 -2.000377 -1.1394343
## X42 -1.647864 -2.259135 -0.8915981
## X43 -1.665336 -2.357788 -0.9942523
## X44 -1.659412 -2.259135 -1.8325815
## X45 -1.724319 -1.784998 -1.0498221
## X46 -1.696685 -2.081112 -1.2729657
## X47 -1.717157 -2.463991 -1.2039728
## X48 -1.755051 -2.000377 -1.8325815
## X50 -1.747018 -2.081112 -1.3862944
## X51 -1.843795 -2.357788 -2.2072749
## X53 -1.585271 -2.357788 -1.2378744
## X55 -1.690161 -2.357788 -1.9661129
## X56 -1.683772 -1.852753 -1.6607312
## X57 -1.690161 -1.924411 -0.2744368
## X59 -1.671366 -2.000377 -1.1711830
## X60 -1.755051 -3.338046 -1.5606477
## X61 -1.724319 -2.703458 -1.3862944
## X62 -1.653590 -2.703458 -1.9661129
## X63 -1.575736 -2.578792 -1.1394343
## X64 -1.647864 -2.000377 -0.6161861
## X65 -1.739232 -2.463991 -1.9661129
## X67 -1.683772 -2.357788 -0.6539265
## X68 -1.817791 -2.167156 -0.3011051
## X69 -1.755051 -3.154089 -1.6094379
## X70 -1.755051 -2.463991 -1.8971200
## X71 -1.671366 -2.000377 -1.4696760
## X72 -1.690161 -1.395242 -0.5447272
## X73 -1.595031 -1.924411 -1.5141277
## X74 -1.710172 -2.357788 -1.6094379
## X75 -1.690161 -1.924411 -0.7765288
## X76 -1.600000 -1.720797 -1.4696760
## X77 -1.631218 -2.167156 -1.5606477
## X78 -1.610128 -1.262002 -0.7985077
## X80 -1.665336 -2.259135 -1.5141277
## X81 -1.631218 -2.259135 -1.2378744
## X82 -1.724319 -2.578792 -2.2072749
## X83 -1.585271 -2.081112 -0.4942963
## X84 -1.659412 -2.357788 -1.3862944
## X85 -1.671366 -2.000377 -0.9416085
## X86 -1.610128 -1.546611 -1.2039728
## X88 -1.677510 -1.720797 -0.7550226
## X90 -1.790238 -3.154089 -0.8439701
## X93 -1.755051 -2.000377 -1.5606477
## X94 -1.642229 -1.220997 -0.5108256
## X95 -1.665336 -2.167156 -1.9661129
## X96 -1.690161 -2.357788 -1.7719568
## X97 -1.710172 -2.578792 -2.5010360
## X98 -1.790238 -2.703458 -1.8971200
## X99 -1.690161 -2.259135 -1.2729657
## X100 -1.659412 -2.357788 -1.7719568
## X103 -1.731672 -2.000377 -1.6607312
## X104 -1.653590 -2.259135 -1.6607312
## X105 -1.671366 -2.167156 -2.5133061
## X107 -1.731672 -2.463991 -1.2729657
## X108 -1.665336 -2.000377 -1.8325815
## X109 -1.933668 -3.338046 -2.1202635
## X110 -1.710172 -2.988944 -1.6094379
## X111 -1.710172 -2.357788 -2.0402208
## X112 -1.653590 -1.852753 -0.6161861
## X113 -1.600000 -1.720797 -1.3862944
## X114 -1.739232 -2.703458 -1.5141277
## X115 -1.747018 -2.000377 -0.7133499
## X117 -1.710172 -2.357788 -2.0402208
## X118 -1.636682 -2.259135 -1.7719568
## X121 -1.647864 -2.081112 -1.2039728
## X123 -1.790238 -2.578792 -1.6094379
## X124 -1.631218 -2.167156 -1.7147984
## X126 -1.647864 -2.463991 -1.6094379
## X128 -1.590122 -1.720797 -1.5606477
## X129 -1.717157 -2.578792 -1.3470736
## X130 -1.677510 -2.000377 -1.3093333
## X131 -1.690161 -2.578792 -1.3862944
## X132 -1.710172 -2.167156 -1.5141277
## X133 -1.771965 -1.924411 -1.7147984
## X134 -1.755051 -2.000377 -0.7985077
## X135 -1.739232 -2.357788 -1.7719568
## X136 -1.763357 -2.578792 -1.7719568
## X137 -1.703352 -2.703458 -1.8325815
## X139 -1.696685 -2.839536 -1.0498221
## X140 -1.710172 -2.259135 -1.9661129
## X141 -1.690161 -2.081112 -1.8971200
## X143 -1.724319 -2.081112 -1.1086626
## X144 -1.755051 -2.578792 -1.4696760
## X145 -1.815609 -2.703458 -2.1202635
## X146 -1.677510 -1.852753 -1.3862944
## X147 -1.690161 -2.081112 -1.9661129
## X148 -1.631218 -1.852753 -0.5447272
## X149 -1.696685 -2.703458 -1.3093333
## X152 -1.571048 -2.167156 -0.9675840
## X153 -1.696685 -1.443483 -0.8209806
## X154 -1.703352 -2.578792 -1.6607312
## X155 -1.710172 -2.259135 -1.6607312
## X156 -1.610128 -1.924411 -1.2729657
## X157 -1.710172 -2.167156 -1.5141277
## X158 -1.690161 -2.578792 -2.0402208
## X159 -1.653590 -2.259135 -1.3862944
## X160 -1.703352 -2.081112 -1.8971200
## X161 -1.590122 -1.924411 -1.0216512
## X162 -1.642229 -2.357788 -1.8325815
## X163 -1.690161 -2.703458 -2.3025851
## X165 -1.653590 -2.463991 -2.3025851
## X166 -1.671366 -2.578792 -1.7719568
## X167 -1.615292 -2.259135 -1.3470736
## X168 -1.631218 -2.357788 -1.2039728
## X169 -1.690161 -2.578792 -1.3470736
## X170 -1.690161 -2.259135 -1.5606477
## X171 -1.659412 -1.720797 -1.0498221
## X172 -1.696685 -2.578792 -1.7719568
## X174 -1.575736 -1.659842 -0.7339692
## X175 -1.665336 -1.852753 -1.5141277
## X176 -1.600000 -2.463991 -0.7985077
## X177 -1.690161 -2.259135 -1.5606477
## X178 -1.696685 -2.703458 -1.1711830
## X179 -1.600000 -1.493883 -1.3862944
## X180 -1.771965 -2.167156 -1.2729657
## X181 -1.710172 -2.703458 -1.0216512
## X182 -1.724319 -2.357788 -1.5141277
## X183 -1.755051 -2.000377 -2.4304185
## X184 -1.690161 -2.259135 -1.2729657
## X185 -1.683772 -2.081112 -0.7133499
## X186 -1.710172 -2.463991 -1.6607312
## X189 -1.696685 -2.578792 -2.0402208
## X190 -1.817791 -2.167156 -0.9942523
## X191 -1.710172 -2.357788 -1.7719568
## X192 -1.642229 -2.081112 -1.9661129
## X193 -1.671366 -1.852753 -1.3862944
## X194 -1.653590 -2.357788 -1.5141277
## X195 -1.585271 -2.167156 -1.8325815
## X197 -1.690161 -2.000377 -1.5606477
## X198 -1.724319 -2.578792 -1.8971200
## X200 -1.696685 -2.703458 -2.3538784
## X201 -1.665336 -2.357788 -0.8915981
## X202 -1.677510 -2.988944 -1.6094379
## X205 -1.763357 -2.167156 -1.6607312
## X208 -1.724319 -2.259135 -1.5141277
## X210 -1.665336 -2.167156 -1.8325815
## X212 -1.696685 -2.578792 -2.2072749
## X213 -1.530958 -2.081112 -1.5141277
## X214 -1.486191 -2.081112 -1.1394343
## X215 -1.755051 -2.463991 -1.2378744
## X216 -1.647864 -2.167156 -1.3470736
## X218 -1.724319 -2.000377 -0.8675006
## X219 -1.683772 -2.463991 -1.3862944
## X220 -1.590122 -2.463991 -1.4271164
## X223 -1.615292 -2.463991 -1.4696760
## X224 -1.642229 -2.578792 -1.9661129
## X225 -1.610128 -2.259135 -2.3859667
## X226 -1.690161 -2.357788 -1.3862944
## X227 -1.710172 -1.659842 -1.7719568
## X228 -1.739232 -2.463991 -2.0402208
## X229 -1.771965 -2.463991 -1.8325815
## X230 -1.710172 -1.924411 -0.7133499
## X231 -1.690161 -2.357788 -1.7719568
## X232 -1.724319 -2.357788 -1.8325815
## X233 -1.755051 -2.703458 -1.6094379
## X234 -1.696685 -1.720797 -2.0402208
## X236 -1.747018 -2.703458 -2.2072749
## X237 -1.647864 -1.924411 -1.4271164
## X239 -1.710172 -2.839536 -1.4271164
## X240 -1.659412 -2.463991 -1.4696760
## X241 -1.665336 -2.357788 -2.1202635
## X242 -1.631218 -1.262002 -1.8325815
## X243 -1.683772 -2.357788 -1.0498221
## X244 -1.600000 -2.081112 -1.5141277
## X245 -1.771965 -2.988944 -1.0788097
## X246 -1.696685 -1.924411 -1.1086626
## X247 -1.724319 -2.578792 -1.5606477
## X249 -1.717157 -2.259135 -1.7147984
## X250 -1.696685 -2.463991 -1.7147984
## X251 -1.671366 -2.259135 -1.7719568
## X253 -1.590122 -2.000377 -0.8915981
## X254 -1.605032 -1.546611 -1.3862944
## X255 -1.647864 -1.852753 -1.4271164
## X256 -1.747018 -2.578792 -1.9661129
## X257 -1.763357 -2.463991 -1.4696760
## X258 -1.671366 -1.784998 -0.6539265
## X260 -1.615292 -2.081112 -1.1711830
## X261 -1.717157 -2.703458 -1.6607312
## X262 -1.683772 -2.839536 -2.2072749
## X263 -1.780911 -1.852753 -2.0402208
## X264 -1.590122 -1.784998 -1.0788097
## X265 -1.739232 -2.463991 -1.7147984
## X267 -1.665336 -1.852753 -1.7147984
## X268 -1.677510 -2.357788 -1.6094379
## X269 -1.665336 -2.578792 -1.3470736
## X270 -1.690161 -2.357788 -1.7147984
## X271 -1.710172 -1.852753 -0.7550226
## X272 -1.631218 -2.259135 -1.5606477
## X273 -1.575736 -2.000377 -1.1086626
## X274 -1.717157 -2.463991 -1.8325815
## X275 -1.659412 -2.357788 -1.6094379
## X277 -1.677510 -2.357788 -1.4271164
## X278 -1.739232 -2.081112 -1.7147984
## X279 -1.710172 -2.703458 -1.6094379
## X281 -1.717157 -2.000377 -1.3862944
## X282 -1.690161 -2.081112 -1.5141277
## X283 -1.642229 -1.852753 -1.7719568
## X287 -1.690161 -2.259135 -2.2072749
## X289 -1.703352 -2.703458 -1.9661129
## X290 -1.763357 -2.463991 -1.8971200
## X291 -1.620527 -2.259135 -0.7133499
## X292 -1.423806 -1.546611 -1.4271164
## X294 -1.739232 -2.703458 -1.0216512
## X297 -1.717157 -2.167156 -1.7147984
## X298 -1.703352 -2.167156 -1.3862944
## X299 -1.671366 -2.000377 -1.6607312
## X301 -1.731672 -2.357788 -1.8971200
## X302 -1.771965 -2.703458 -1.3862944
## X303 -1.739232 -2.081112 -1.2039728
## X304 -1.671366 -2.167156 -1.9661129
## X305 -1.710172 -1.784998 -1.5141277
## X306 -1.724319 -1.659842 -0.5447272
## X307 -1.595031 -1.924411 -1.4696760
## X308 -1.631218 -2.259135 -1.7147984
## X311 -1.747018 -2.000377 -1.1394343
## X312 -1.683772 -2.167156 -1.3470736
## X313 -1.690161 -2.259135 -1.9661129
## X314 -1.724319 -2.578792 -2.3434071
## X315 -1.677510 -2.167156 -1.2729657
## X316 -1.690161 -2.463991 -2.1202635
## X317 -1.659412 -2.259135 -1.7719568
## X320 -1.671366 -1.659842 -1.0788097
## X321 -1.813452 -2.357788 -1.8971200
## X322 -1.731672 -1.546611 -0.9942523
## X323 -1.690161 -2.259135 -1.2729657
## X324 -1.620527 -2.357788 -1.8971200
## X325 -1.710172 -2.167156 -1.7147984
## X326 -1.665336 -2.463991 -1.5141277
## X327 -1.600000 -1.601860 -0.8915981
## X329 -1.717157 -2.357788 -1.4271164
## X330 -1.724319 -1.720797 -1.5141277
## X331 -1.771965 -2.578792 -1.7147984
## X332 -1.690161 -2.259135 -1.0216512
## X333 -1.642229 -2.167156 -1.8971200
## RANTES Resistin S100b SGOT SHBG SOD
## X1 -6.214608 -16.475315 1.5618560 -0.94160854 -1.897120 5.609472
## X2 -6.938214 -16.025283 1.7566212 -0.65392647 -1.560648 5.814131
## X3 -6.645391 -16.475315 1.4357282 0.33647224 -2.207275 5.723585
## X5 -6.319969 -11.092838 1.3012972 0.09531018 -2.430418 5.655992
## X6 -6.502290 -11.291369 1.0546073 -0.31471074 -2.645075 4.543295
## X7 -6.812445 -20.660678 1.3012972 -0.69314718 -3.123566 5.509388
## X8 -6.377127 -6.048172 1.0546073 -0.15082289 -2.396896 4.532599
## X9 -6.502290 -28.434991 1.0011977 -0.51082562 -1.714798 4.941642
## X11 -6.502290 -11.291369 1.7566212 -0.06187540 -1.560648 5.488938
## X12 -6.571283 -14.824999 1.5206598 -0.31471074 -2.312635 5.129899
## X14 -6.214608 -16.954608 1.5206598 -0.75502258 -2.577022 5.262690
## X16 -6.032287 -15.202379 1.1570961 -0.10536052 -1.108663 5.605802
## X17 -6.265901 -10.901667 1.5206598 -0.91629073 -2.207275 5.030438
## X18 -6.812445 -24.395099 1.1065417 -0.44628710 -2.813411 4.882802
## X19 -6.165818 -16.475315 0.5751964 -1.27296568 -2.207275 4.584967
## X20 -5.914504 -10.717434 1.5206598 -0.17435339 -2.847312 5.424950
## X21 -6.502290 -14.824999 0.7704814 -0.26136476 -2.353878 5.262690
## X22 -6.032287 -32.139553 1.1570961 -0.30110509 -3.688879 6.045005
## X23 -6.502290 -16.954608 1.7566212 -0.40047757 -2.900422 5.262690
## X24 -6.645391 -22.351393 1.5618560 -0.77652879 -3.244194 5.834811
## X25 -6.812445 -23.322142 1.3471128 -0.52763274 -1.897120 5.023881
## X26 -6.571283 -13.807280 1.5206598 -0.34249031 -2.441847 5.468060
## X28 -6.502290 -19.235033 1.3012972 0.09531018 -2.207275 5.468060
## X29 -6.725434 -24.395099 0.8309909 -0.52763274 -3.611918 4.779123
## X30 -6.980326 -22.351393 1.0011977 -0.73396918 -2.207275 4.941642
## X31 -6.502290 -18.014017 1.2063562 -0.24846136 -2.430418 5.192957
## X34 -6.571283 -18.014017 0.8895156 -0.57981850 -3.015935 5.062595
## X35 -6.725434 -15.202379 1.4357282 -0.40047757 -2.813411 4.859812
## X36 -6.502290 -14.467762 1.3012972 -0.71334989 -2.040221 5.442418
## X37 -6.502290 -16.025283 1.4786312 -0.24846136 -2.040221 5.521461
## X38 -6.377127 -26.925298 1.0011977 -0.22314355 -2.563950 5.327876
## X39 -6.907755 -23.322142 1.5618560 -0.51082562 -2.501036 5.634790
## X40 -6.812445 -18.601960 1.0546073 -0.69314718 -1.771957 5.342334
## X41 -6.502290 -8.576675 1.3919052 -0.71334989 -1.609438 5.010635
## X42 -6.377127 -16.954608 0.8309909 0.09531018 -2.207275 5.288267
## X43 -6.725434 -25.588488 1.1065417 -1.10866262 -2.333044 4.875197
## X44 -5.843045 -9.592564 1.1570961 -0.71334989 -2.302585 5.472271
## X45 -6.437752 -12.782746 1.5618560 -0.34249031 -2.748872 5.693732
## X46 -6.907755 -16.954608 1.3471128 -0.73396918 -3.079114 5.648974
## X47 -6.917806 -17.466301 0.5047530 -0.23572233 -2.207275 4.919981
## X48 -6.265901 -18.014017 1.6808260 -0.63487827 -2.501036 5.634790
## X50 -6.319969 -10.202587 1.1065417 -0.54472718 -2.847312 5.379897
## X51 -6.571283 -11.092838 1.1570961 0.26236426 -1.347074 5.730100
## X53 -6.319969 -3.316155 1.0546073 -0.96758403 -2.333044 5.398163
## X55 -6.502290 -18.601960 1.2543998 -0.77652879 -2.465104 5.455321
## X56 -6.725434 -13.807280 1.0011977 -0.49429632 -2.120264 5.472271
## X57 -6.437752 -16.025283 0.9462067 -0.09431068 -2.207275 5.517453
## X59 -5.843045 -13.807280 1.7935512 -0.19845094 -2.302585 5.480639
## X60 -7.106206 -25.588488 0.9462067 0.26236426 -2.302585 5.068904
## X61 -6.907755 -18.601960 1.0546073 0.47000363 -2.501036 4.962845
## X62 -7.208860 -24.395099 1.2543998 -0.54472718 -3.411248 4.672829
## X63 -6.377127 -18.601960 1.3471128 -0.07257069 -3.146555 5.433722
## X64 -5.546779 -12.168957 1.8656036 -0.40047757 -1.386294 5.036953
## X65 -6.725434 -20.660678 1.3919052 -0.59783700 -3.036554 5.476464
## X67 -6.907755 -25.588488 1.3919052 -0.26136476 -2.207275 5.831882
## X68 -6.319969 -20.660678 1.3012972 -0.30110509 -2.343407 5.389072
## X69 -7.058578 -16.475315 0.5751964 -0.99425227 -2.956512 4.875197
## X70 -6.812445 -19.918999 1.2063562 -0.54472718 -2.302585 5.564520
## X71 -6.437752 -18.014017 1.1570961 -0.61618614 -3.123566 5.384495
## X72 -6.032287 -11.712400 1.4786312 -0.43078292 -1.171183 6.045005
## X73 -6.119298 -9.737717 1.4786312 -0.59783700 -2.796881 5.905362
## X74 -6.812445 -15.601770 1.0546073 -0.46203546 -2.396896 4.418841
## X75 -6.074846 -21.468210 1.4786312 -0.69314718 -1.171183 4.955827
## X76 -6.319969 -3.509845 1.7190552 -0.19845094 -2.718101 5.723585
## X77 -6.214608 -12.931637 1.9353985 -0.96758403 -1.897120 5.420535
## X78 -6.119298 -12.931637 1.2543998 -0.61618614 -1.771957 5.929589
## X80 -6.502290 -18.601960 1.5206598 -0.96758403 -3.575551 5.273000
## X81 -6.265901 -25.588488 1.1065417 -0.31471074 -2.207275 5.379897
## X82 -6.437752 -20.660678 1.8656036 -0.31471074 -1.427116 5.273000
## X83 -6.319969 -13.807280 1.2543998 -0.89159812 -3.442019 4.934474
## X84 -6.645391 -20.660678 1.2543998 -0.61618614 -2.764621 5.225747
## X85 -6.214608 -19.235033 1.6022588 -0.22314355 -2.718101 5.641907
## X86 -6.265901 -9.737717 1.5618560 -0.82098055 -2.207275 5.476464
## X88 -6.502290 -11.712400 1.3919052 -0.11653382 -2.207275 5.451038
## X90 -5.809143 -26.925298 0.3540404 -0.69314718 -3.296837 4.317488
## X93 -6.437752 -11.935945 1.3012972 -0.59783700 -1.771957 5.192957
## X94 -6.032287 -9.887603 1.7190552 -0.03045921 -1.560648 6.317165
## X95 -6.645391 -18.014017 1.6022588 0.74193734 -1.714798 4.828314
## X96 -7.002066 -18.601960 1.3919052 -0.51082562 -2.364460 5.624018
## X97 -6.571283 -19.918999 0.9462067 0.00000000 -2.207275 5.869297
## X98 -6.938214 -22.351393 0.7704814 -0.04082199 -2.120264 5.056246
## X99 -6.119298 -11.935945 1.2543998 -0.82098055 -2.441847 5.855072
## X100 -6.571283 -24.395099 1.3012972 -0.11653382 -2.764621 5.572154
## X103 -6.074846 -12.168957 1.2543998 -0.24846136 -2.207275 6.222576
## X104 -6.571283 -23.322142 1.3012972 -0.44628710 -2.577022 5.497168
## X105 -6.571283 -28.434991 1.5206598 -0.27443685 -1.897120 5.680173
## X107 -6.319969 -19.235033 1.2063562 -0.34249031 -2.441847 5.579730
## X108 -6.571283 -18.014017 1.5206598 -0.24846136 -2.385967 5.308268
## X109 -6.812445 -21.468210 1.0546073 0.26236426 -3.772261 5.153292
## X110 -6.725434 -19.918999 1.0546073 -0.40047757 -2.780621 4.859812
## X111 -6.502290 -19.918999 1.2063562 -0.63487827 -2.551046 5.525453
## X112 -6.645391 -11.497723 1.6022588 -0.40047757 -1.203973 5.525453
## X113 -6.214608 -13.209714 1.6419042 -0.31471074 -1.771957 5.468060
## X114 -7.002066 -25.588488 0.8309909 0.09531018 -2.645075 4.836282
## X115 -6.571283 -14.824999 1.1065417 0.33647224 -1.609438 5.105945
## X117 -5.572754 -12.931637 1.6022588 -0.69314718 -1.386294 5.572154
## X118 -6.319969 -14.129014 1.5618560 -0.77652879 -2.590267 5.765191
## X121 -6.437752 -13.501240 1.3012972 -0.18632958 -1.560648 5.799093
## X123 -7.058578 -32.139553 0.5047530 0.58778666 -1.832581 4.770685
## X124 -6.437752 -18.014017 0.7704814 -0.63487827 -3.352407 5.379897
## X126 -6.645391 -20.660678 0.5751964 -1.13943428 -2.577022 5.468060
## X128 -6.074846 -16.475315 1.5618560 -0.21072103 -1.309333 5.648974
## X129 -6.725434 -22.351393 0.8309909 -0.75502258 -2.764621 4.976734
## X130 -6.377127 -11.092838 1.1570961 -0.63487827 -2.207275 5.529429
## X131 -7.156217 -26.925298 1.3919052 -0.69314718 -2.538307 5.262690
## X132 -6.725434 -12.931637 1.1065417 -0.99425227 -2.780621 5.198497
## X133 -6.571283 -12.168957 1.5206598 -0.23572233 -1.469676 5.308268
## X134 -6.067933 -10.901667 1.7935512 -0.94160854 -2.407946 5.497168
## X135 -6.725434 -14.129014 1.0546073 -0.09431068 -1.171183 5.780744
## X136 -6.319969 -3.509845 0.9462067 -0.57981850 -2.302585 4.828314
## X137 -6.725434 -21.468210 1.3471128 -0.47803580 -4.135167 5.153292
## X139 -7.094085 -20.660678 1.0546073 -0.23572233 -2.538307 5.111988
## X140 -6.571283 -9.737717 1.2543998 -0.04082199 -2.673649 5.529429
## X141 -5.843045 -18.014017 1.2543998 -0.84397007 -2.207275 5.468060
## X143 -6.265901 -21.468210 0.6427959 -0.67334455 -2.040221 4.820282
## X144 -6.645391 -18.601960 1.2063562 -0.47803580 -2.407946 5.262690
## X145 -6.917806 -28.434991 0.8895156 -0.18632958 -2.488915 5.093750
## X146 -5.878136 -13.209714 1.2063562 -0.35667494 -1.771957 5.613128
## X147 -6.812445 -2.239355 1.4786312 -0.21072103 -2.322788 5.598422
## X148 -6.165818 -16.025283 1.8298706 0.26236426 -2.207275 5.342334
## X149 -6.917806 -23.322142 0.8309909 -0.43078292 -2.764621 5.164786
## X152 -6.571283 -15.601770 1.3919052 0.09531018 -2.207275 5.869297
## X153 -6.502290 -19.235033 2.3725662 -0.41551544 -2.207275 5.379897
## X154 -6.812445 -22.351393 1.3919052 -0.40047757 -3.611918 5.198497
## X155 -6.645391 -20.660678 1.2543998 -0.37106368 -3.244194 5.262690
## X156 -6.319969 -15.601770 1.6808260 -0.67334455 -2.577022 5.583496
## X157 -6.377127 -13.807280 1.0546073 -0.67334455 -2.577022 5.278115
## X158 -6.917806 -20.660678 1.1065417 -0.51082562 -2.813411 5.517453
## X159 -6.377127 -20.460441 1.1065417 -0.34249031 -3.442019 5.170484
## X160 -6.571283 -8.047964 1.7935512 0.53062825 -2.631089 5.940171
## X161 -6.319969 -10.368242 1.3012972 -0.41551544 -3.270169 5.669881
## X162 -6.377127 -20.660678 1.4357282 -1.17118298 -2.975930 5.686975
## X163 -6.812445 -25.588488 1.3012972 0.09531018 -2.703063 4.948760
## X165 -6.571283 -19.235033 1.3919052 -0.12783337 -2.577022 5.796058
## X166 -6.502290 -28.434991 1.2063562 -0.26136476 -3.611918 5.323010
## X167 -6.502290 -12.666051 1.6022588 -0.89159812 -1.897120 5.869297
## X168 -6.917806 -18.601960 1.5618560 -0.65392647 -2.918771 5.365976
## X169 -7.118476 -20.660678 0.6427959 -0.40047757 -2.513306 5.164786
## X170 -6.571283 -15.601770 1.5618560 -0.35667494 -2.207275 5.105945
## X171 -6.571283 -10.717434 1.5206598 -0.84397007 -2.501036 5.517453
## X172 -6.938214 -22.351393 0.8309909 0.09531018 -2.040221 5.147494
## X174 -6.214608 -10.539746 1.3012972 -0.43078292 -2.207275 5.262690
## X175 -6.265901 -10.539746 1.3919052 -0.27443685 -1.771957 5.669881
## X176 -5.776353 -16.954608 0.9462067 -0.77652879 -1.771957 5.075174
## X177 -6.725434 -19.918999 0.9462067 -0.35667494 -2.353878 5.433722
## X178 -7.143478 -19.235033 0.6427959 -0.31471074 -2.882404 4.905275
## X179 -6.214608 -6.464363 1.7190552 -0.69314718 -2.096274 5.164786
## X180 -6.074846 -14.824999 1.2543998 -0.18632958 -2.040221 5.572154
## X181 -6.645391 -26.925298 0.7078153 -1.13943428 -3.324236 4.859812
## X182 -6.645391 -25.588488 0.9462067 -0.32850407 -2.603690 5.187386
## X183 -6.938214 -18.014017 0.6427959 -0.96758403 -1.609438 4.553877
## X184 -6.377127 -16.025283 1.3012972 -0.94160854 -2.796881 5.680173
## X185 -6.571283 -13.209714 1.3919052 -0.96758403 -3.079114 5.525453
## X186 -7.156217 -21.468210 1.3471128 -0.71334989 -2.882404 5.198497
## X189 -6.645391 -18.601960 0.9462067 -0.43078292 -2.364460 5.765191
## X190 -6.377127 -23.322142 1.3471128 0.18232156 -2.538307 5.293305
## X191 -6.165818 -23.322142 1.4786312 -0.52763274 -3.101093 5.389072
## X192 -6.265901 -19.918999 1.8656036 -0.57981850 -2.631089 6.079933
## X193 -6.265901 -14.824999 1.7190552 -0.34249031 -2.407946 5.613128
## X194 -6.319969 -17.466301 0.9462067 -0.84397007 -1.771957 4.927254
## X195 -6.265901 -21.468210 1.7190552 -0.44628710 -2.501036 6.269096
## X197 -6.265901 -11.935945 1.3012972 -0.22314355 -2.302585 5.438079
## X198 -6.907755 -23.322142 0.6427959 -0.63487827 -2.302585 5.117994
## X200 -6.725434 -19.918999 1.9695015 -0.26136476 -2.937463 5.262690
## X201 -6.265901 -22.351393 0.9462067 -0.11653382 -3.352407 5.068904
## X202 -6.812445 -34.966595 0.9462067 -0.35667494 -3.194183 4.867534
## X205 -6.812445 -15.601770 1.6419042 0.40546511 -2.937463 5.710427
## X208 -6.502290 -26.925298 1.1570961 -0.27443685 -2.396896 5.181784
## X210 -6.502290 -2.450735 1.6808260 -0.18632958 -2.813411 5.513429
## X212 -6.119298 -22.351393 1.4357282 -0.34249031 -2.718101 5.209486
## X213 -6.725434 -18.014017 1.4357282 -0.19845094 -1.897120 5.513429
## X214 -6.265901 -10.539746 0.7704814 -0.73396918 -3.057608 5.634790
## X215 -7.156217 -16.475315 1.1065417 -0.15082289 -3.411248 5.451038
## X216 -6.437752 -12.931637 1.6022588 -0.47803580 -2.396896 5.023881
## X218 -6.319969 -23.322142 1.3919052 -0.46203546 -1.897120 4.836282
## X219 -6.812445 -19.235033 0.8309909 -0.02020271 -2.764621 5.273000
## X220 -7.222466 -30.156007 0.6427959 -0.40047757 -4.074542 5.293305
## X223 -6.907755 -14.467762 1.6808260 -0.41551544 -2.302585 5.455321
## X224 -6.980326 -13.807280 1.0546073 -0.13926207 -2.551046 5.393628
## X225 -6.725434 -25.588488 1.9007725 -0.61618614 -1.897120 6.171701
## X226 -6.265901 -12.931637 1.6022588 -0.40047757 -2.780621 5.991465
## X227 -6.725434 -16.475315 1.5618560 -0.99425227 -1.660731 5.204007
## X228 -6.938214 -13.501240 0.8309909 -0.24846136 -3.473768 4.897840
## X229 -7.002066 -25.588488 1.0011977 -0.63487827 -3.540459 5.093750
## X230 -6.502290 -11.935945 0.8309909 -0.21072103 -1.771957 5.468060
## X231 -6.571283 -16.025283 1.5206598 -0.40047757 -2.764621 5.075174
## X232 -6.437752 -26.925298 0.9462067 -0.04082199 -2.430418 5.056246
## X233 -6.725434 -21.468210 0.8895156 0.26236426 -2.407946 4.744932
## X234 -6.725434 -14.467762 1.1065417 -0.61618614 -1.560648 5.298317
## X236 -6.907755 -23.322142 1.0546073 -0.52763274 -2.501036 5.572154
## X237 -6.119298 -14.467762 1.4357282 0.00000000 -1.897120 5.236442
## X239 -6.725434 -18.601960 0.1873999 -0.62623564 -2.813411 4.430817
## X240 -6.907755 -14.129014 0.9462067 -1.30933332 -3.218876 4.744932
## X241 -5.843045 -21.468210 1.1065417 -0.94160854 -1.139434 5.662960
## X242 -6.502290 -9.592564 1.4357282 -0.52763274 -1.660731 4.553877
## X243 -6.571283 -21.468210 1.1570961 -0.17435339 -2.120264 4.983607
## X244 -5.991465 -14.824999 1.0546073 -0.49429632 -2.207275 5.517453
## X245 -6.645391 -13.501240 1.0546073 -0.37106368 -3.729701 4.330733
## X246 -6.165818 -13.209714 0.9462067 -0.47803580 -2.040221 5.075174
## X247 -6.725434 -17.466301 0.7704814 0.00000000 -3.170086 4.859812
## X249 -6.377127 -16.954608 1.8298706 -0.19845094 -2.718101 6.082219
## X250 -6.437752 -17.466301 1.6022588 -0.43078292 -2.513306 5.347108
## X251 -6.812445 -24.395099 1.1065417 -0.22314355 -2.918771 5.303305
## X253 -6.377127 -16.025283 1.3471128 -0.40047757 -2.551046 5.398163
## X254 -6.214608 -12.931637 1.7935512 -0.67334455 -2.603690 6.317165
## X255 -5.626821 -18.601960 1.3471128 -0.56211892 -2.120264 5.703782
## X256 -6.812445 -25.588488 1.1570961 -0.26136476 -2.718101 5.332719
## X257 -6.725434 -25.588488 1.0546073 -0.17435339 -2.780621 4.867534
## X258 -6.265901 -4.873381 1.6022588 -0.54472718 -2.040221 5.891644
## X260 -6.377127 -23.322142 1.3471128 -0.34249031 -2.603690 5.420535
## X261 -6.437752 -16.954608 1.0011977 -0.54472718 -2.513306 4.828314
## X262 -6.645391 -23.322142 1.0011977 -0.91629073 -2.441847 4.804021
## X263 -6.165818 -15.202379 1.1570961 -0.26136476 -1.609438 5.446737
## X264 -6.319969 -11.291369 1.5618560 -0.56211892 -2.120264 5.123964
## X265 -6.571283 -23.322142 0.9462067 -0.56211892 -2.419119 5.459586
## X267 -6.437752 -10.539746 1.8298706 -0.10536052 -2.918771 5.616771
## X268 -6.571283 -15.601770 1.3919052 -0.11653382 -2.312635 5.484797
## X269 -6.725434 -28.434991 0.9462067 -0.73396918 -3.218876 5.192957
## X270 -6.319969 -14.824999 0.7704814 0.00000000 -2.563950 5.993961
## X271 -6.319969 -12.666051 1.4786312 -0.38566248 -1.660731 5.402677
## X272 -6.437752 -15.601770 1.4357282 -0.54472718 -2.937463 5.087596
## X273 -6.377127 -12.666051 1.2543998 -0.34249031 -3.352407 5.379897
## X274 -6.074846 -28.434991 0.9462067 -0.26136476 -3.170086 5.170484
## X275 -6.571283 -28.434991 1.7935512 -0.63487827 -2.688248 5.620401
## X277 -6.074846 -14.129014 1.2543998 -0.35667494 -2.488915 5.262690
## X278 -6.119298 -14.467762 0.9462067 -0.34249031 -3.101093 5.451038
## X279 -6.980326 -16.954608 1.4357282 -0.73396918 -2.937463 5.209486
## X281 -6.074846 -3.723928 1.3919052 -0.47803580 -2.631089 5.407172
## X282 -6.165818 -20.660678 1.1570961 -0.31471074 -3.123566 5.252273
## X283 -5.843045 -7.247686 1.4786312 -0.57981850 -1.771957 5.826000
## X287 -6.917806 -18.014017 1.3919052 -0.22314355 -2.120264 5.783825
## X289 -6.812445 -19.235033 0.7078153 -0.27443685 -2.937463 4.787492
## X290 -6.165818 -16.025283 1.0011977 -0.61618614 -3.057608 5.093750
## X291 -6.265901 -20.660678 1.1570961 -0.61618614 -2.207275 5.572154
## X292 -6.165818 -26.925298 1.1065417 -0.89159812 -2.617296 5.669881
## X294 -7.024289 -22.293116 1.0011977 -0.43078292 -3.649659 4.691348
## X297 -6.907755 -16.475315 0.9462067 0.09531018 -2.796881 5.257495
## X298 -7.047017 -22.351393 0.5047530 -0.12783337 -2.673649 5.036953
## X299 -6.571283 -26.925298 0.9462067 0.00000000 -2.476938 5.613128
## X301 -6.502290 -19.235033 0.8309909 -0.02020271 -3.611918 5.220356
## X302 -7.208860 -19.235033 0.9462067 -1.34707365 -3.079114 4.418841
## X303 -6.645391 -18.014017 1.3012972 -0.34249031 -3.101093 5.303305
## X304 -6.645391 -22.351393 1.9695015 -0.06187540 -2.207275 5.755742
## X305 -6.319969 -16.954608 1.6419042 -0.40047757 -1.966113 5.351858
## X306 -6.377127 -12.412086 1.6419042 -0.79850770 -2.302585 5.891644
## X307 -6.319969 -11.497723 1.9695015 -0.82098055 -1.560648 5.755742
## X308 -6.437752 -13.501240 1.5206598 0.09531018 -2.764621 5.501258
## X311 -6.377127 -16.954608 1.0546073 -0.99425227 -1.897120 5.081404
## X312 -6.377127 -18.601960 1.4357282 -0.31471074 -3.101093 5.843544
## X313 -6.319969 -19.235033 1.2543998 0.00000000 -1.771957 5.117994
## X314 -6.571283 -26.925298 0.8895156 0.00000000 -2.476938 5.278115
## X315 -6.571283 -16.475315 1.3471128 -0.73396918 -2.748872 5.117994
## X316 -6.917806 -26.925298 1.1570961 -0.09431068 -2.563950 5.583496
## X317 -6.645391 -21.468210 1.0546073 0.00000000 -2.590267 5.710427
## X320 -6.812445 -13.501240 1.0546073 -0.56211892 -2.302585 5.056246
## X321 -6.938214 -14.467762 1.1570961 -0.61618614 -2.718101 4.394449
## X322 -6.377127 -8.930136 1.8656036 -0.49429632 -1.966113 5.398163
## X323 -6.437752 -12.666051 1.0011977 -0.37106368 -2.882404 5.342334
## X324 -6.812445 -16.954608 1.2063562 -0.54472718 -2.882404 5.093750
## X325 -6.119298 -23.322142 0.9462067 -0.30110509 -2.513306 5.375278
## X326 -6.214608 -13.501240 1.3012972 0.00000000 -2.465104 5.347108
## X327 -6.437752 -14.467762 1.3471128 -0.01005034 -2.733368 5.676754
## X329 -6.571283 -28.434991 0.8895156 -0.69314718 -3.611918 5.693732
## X330 -6.571283 -14.824999 1.2063562 0.09531018 -2.040221 4.875197
## X331 -6.645391 -20.660678 1.0546073 -0.11653382 -1.966113 4.948760
## X332 -6.437752 -18.601960 0.8895156 -0.31471074 -2.847312 4.709530
## X333 -6.214608 -19.918999 1.9007725 -0.13926207 -2.120264 6.013715
## Serum_Amyloid_P Sortilin Stem_Cell_Factor TGF_alpha TIMP_1 TNF_RII
## X1 -5.599422 4.908629 4.174387 8.649098 15.204651 -0.06187540
## X2 -6.119298 5.478731 3.713572 11.331619 11.266499 -0.32850407
## X3 -5.381699 3.810182 3.433987 10.858497 12.282857 -0.41551544
## X5 -5.203007 3.402176 4.060443 8.323453 13.748016 -0.34249031
## X6 -5.115996 2.978813 2.564949 10.008788 11.266499 -0.94160854
## X7 -6.032287 4.037285 3.401197 8.649098 12.422205 -0.77652879
## X8 -5.083206 2.665456 2.772589 10.097662 14.492423 -0.91629073
## X9 -7.013116 2.141223 3.295837 10.777165 10.000000 -0.94160854
## X11 -6.119298 4.802628 2.995732 10.777165 10.489996 -0.51082562
## X12 -5.449140 4.093428 3.091042 9.549474 10.961481 -0.71334989
## X14 -6.032287 3.752748 3.044522 10.008788 13.491933 -0.61618614
## X16 -6.032287 4.479850 3.135494 10.777165 12.696938 -0.28768207
## X17 -5.035953 4.093428 2.944439 9.736307 10.961481 -0.69314718
## X18 -5.952244 2.916923 3.044522 8.542148 10.328828 -0.77652879
## X19 -6.319969 2.341451 2.944439 10.612220 13.620499 -0.79850770
## X20 -4.779524 2.728930 2.772589 10.185606 13.748016 -0.75502258
## X21 -6.645391 2.601557 3.178054 8.649098 10.165525 -0.65392647
## X22 -5.991465 4.315608 3.891820 11.998865 10.961481 -0.04082199
## X23 -6.377127 3.040333 2.890372 9.828139 9.661904 -0.59783700
## X24 -6.032287 6.225224 3.526361 9.454406 11.266499 -0.43078292
## X25 -6.437752 3.695039 2.995732 9.162164 9.832160 -0.82098055
## X26 -6.319969 4.855724 3.367296 10.272644 10.649111 -0.43078292
## X28 -6.265901 4.802628 3.367296 9.358191 11.416408 -0.22314355
## X29 -6.725434 2.791992 2.708050 9.260790 10.165525 -1.02165125
## X30 -6.437752 3.461346 2.708050 7.113891 9.313708 -0.89159812
## X31 -5.298317 2.978813 3.401197 7.982407 13.099669 -0.73396918
## X34 -5.654992 2.916923 2.944439 7.245150 11.856406 -0.65392647
## X35 -6.032287 3.695039 2.944439 9.828139 10.328828 -0.89159812
## X36 -4.919881 3.342694 3.401197 10.358802 12.696938 -0.67334455
## X37 -6.214608 4.802628 3.367296 9.549474 10.806248 -0.30110509
## X38 -6.265901 3.520211 3.663562 9.454406 12.000000 -0.65392647
## X39 -6.074846 5.325310 3.583519 10.097662 12.142136 -0.46203546
## X40 -6.074846 3.101492 3.258097 9.358191 12.966630 -0.44628710
## X41 -5.099467 3.924249 3.178054 10.612220 13.748016 -0.75502258
## X42 -6.319969 3.637051 3.091042 10.008788 10.961481 -0.69314718
## X43 -6.725434 3.282892 3.044522 7.623847 10.328828 -0.86750057
## X44 -6.165818 4.093428 3.496508 10.858497 13.748016 -0.24846136
## X45 -5.776353 3.461346 3.688879 11.331619 12.142136 -0.02020271
## X46 -6.032287 4.093428 3.637586 11.176634 13.099669 -0.38566248
## X47 -6.725434 2.472433 2.944439 8.211578 10.000000 -0.79850770
## X48 -6.377127 5.325310 4.060443 9.162164 11.856406 -0.27443685
## X50 -6.074846 5.170380 3.555348 10.858497 11.266499 -0.54472718
## X51 -6.437752 3.578777 3.295837 9.918956 10.649111 -0.38566248
## X53 -5.991465 3.342694 3.258097 10.097662 13.620499 -0.71334989
## X55 -6.265901 3.520211 3.135494 10.858497 11.266499 -0.63487827
## X56 -5.713833 3.867347 3.178054 11.331619 14.370706 -0.63487827
## X57 -5.991465 4.370576 3.891820 11.484041 14.613248 -0.31471074
## X59 -5.878136 3.867347 3.135494 11.018969 14.000000 -0.30110509
## X60 -6.319969 2.275226 2.890372 8.097921 12.560220 -0.59783700
## X61 -6.377127 2.791992 3.044522 7.623847 10.165525 -1.04982212
## X62 -6.377127 2.472433 2.890372 7.623847 9.661904 -1.20397280
## X63 -5.878136 3.402176 3.178054 10.185606 11.564660 -0.27443685
## X64 -5.744604 4.534163 2.772589 10.858497 13.099669 -0.44628710
## X65 -7.385791 4.425322 3.258097 10.528568 10.489996 -0.73396918
## X67 -6.980326 4.425322 3.583519 10.185606 9.832160 -0.18632958
## X68 -5.449140 3.637051 3.526361 10.695079 12.560220 -0.51082562
## X69 -7.505592 2.407182 3.178054 6.842982 9.489125 -1.04982212
## X70 -7.195437 3.924249 3.367296 10.528568 10.328828 -0.61618614
## X71 -6.119298 3.867347 3.258097 10.358802 12.000000 -0.46203546
## X72 -5.386185 3.980894 4.174387 11.781632 16.439089 0.33647224
## X73 -5.952244 3.924249 3.713572 10.939092 12.560220 -0.03045921
## X74 -5.914504 1.866476 2.251292 6.979888 10.489996 -1.34707365
## X75 -5.339139 2.728930 2.890372 9.358191 15.320508 -0.67334455
## X76 -5.654992 5.427755 3.555348 10.939092 14.970563 0.00000000
## X77 -5.626821 4.315608 3.401197 10.528568 13.874508 -0.41551544
## X78 -5.878136 4.695848 3.951244 11.559326 14.970563 -0.06187540
## X80 -5.496768 4.315608 3.526361 8.649098 10.489996 -0.65392647
## X81 -6.265901 4.370576 2.944439 10.444102 10.806248 -0.59783700
## X82 -6.265901 4.315608 3.401197 8.858503 11.266499 -0.44628710
## X83 -5.744604 3.867347 3.465736 9.643429 11.266499 -0.82098055
## X84 -6.214608 3.637051 2.944439 8.211578 10.649111 -0.57981850
## X85 -5.878136 5.170380 3.496508 9.260790 13.491933 -0.31471074
## X86 -5.318520 4.479850 3.555348 10.777165 14.124515 -0.26136476
## X88 -5.914504 3.520211 3.555348 8.961066 14.000000 -0.30110509
## X90 -6.502290 1.653813 2.564949 8.542148 8.954451 -1.38629436
## X93 -5.991465 2.978813 3.555348 10.272644 13.620499 -0.24846136
## X94 -5.546779 4.204987 3.871201 10.858497 18.880613 0.47000363
## X95 -6.571283 3.867347 2.890372 8.323453 9.661904 -0.63487827
## X96 -6.032287 4.749337 3.401197 10.185606 11.416408 -0.63487827
## X97 -6.812445 4.425322 3.367296 10.444102 10.328828 -0.51082562
## X98 -6.645391 3.040333 3.044522 7.245150 9.489125 -1.10866262
## X99 -5.843045 3.402176 3.761200 10.358802 14.733201 -0.19845094
## X100 -5.914504 5.066223 3.663562 11.854592 11.114877 -0.49429632
## X103 -6.377127 3.810182 4.060443 12.211323 13.362291 -0.05129329
## X104 -5.426151 3.040333 3.784190 11.559326 12.696938 -0.44628710
## X105 -6.502290 4.908629 3.135494 10.272644 11.711309 -0.59783700
## X107 -6.165818 4.479850 3.465736 8.323453 11.266499 -0.59783700
## X108 -5.626821 4.908629 3.713572 8.649098 12.142136 -0.51082562
## X109 -6.571283 3.461346 2.890372 9.454406 10.000000 -0.94160854
## X110 -7.354042 3.402176 3.044522 7.745463 9.313708 -0.89159812
## X111 -6.265901 3.520211 3.637586 10.858497 11.114877 -0.77652879
## X112 -5.449140 4.479850 3.784190 8.097921 12.282857 -0.27443685
## X113 -4.803621 4.908629 3.465736 11.098144 12.696938 0.00000000
## X114 -6.032287 2.665456 2.833213 9.454406 8.770330 -1.07880966
## X115 -5.240048 2.854653 3.091042 9.062271 11.856406 -0.71334989
## X117 -6.502290 5.427755 3.555348 9.162164 10.961481 -0.47803580
## X118 -6.119298 5.118391 3.688879 9.062271 14.733201 -0.18632958
## X121 -6.645391 4.908629 3.583519 10.444102 14.970563 0.09531018
## X123 -6.502290 1.653813 2.484907 7.500000 10.165525 -1.13943428
## X124 -5.809143 2.472433 3.465736 10.939092 12.832397 -0.44628710
## X126 -6.119298 2.854653 3.496508 9.828139 10.000000 -0.84397007
## X128 -5.318520 4.204987 3.295837 10.858497 17.390719 -0.43078292
## X129 -6.502290 3.162299 2.564949 8.858503 10.000000 -1.23787436
## X130 -5.381699 3.867347 3.367296 12.822626 11.114877 -0.44628710
## X131 -6.032287 3.637051 3.295837 9.260790 10.328828 -0.94160854
## X132 -5.035953 2.791992 3.465736 8.433621 11.266499 -0.67334455
## X133 -5.083206 4.260413 3.091042 10.528568 12.422205 -0.57981850
## X134 -5.572754 5.118391 3.828641 12.555529 12.282857 -0.44628710
## X135 -6.319969 3.695039 3.637586 8.211578 12.000000 -0.52763274
## X136 -6.571283 2.728930 3.044522 10.097662 12.696938 -0.65392647
## X137 -6.165818 3.752748 2.890372 9.454406 10.489996 -1.13943428
## X139 -7.130899 3.752748 2.944439 10.097662 10.165525 -0.86750057
## X140 -5.776353 3.695039 3.135494 8.858503 11.114877 -0.69314718
## X141 -5.472671 5.681052 3.465736 10.097662 11.856406 -0.41551544
## X143 -5.713833 2.208489 3.178054 7.982407 11.856406 -1.02165125
## X144 -5.713833 3.637051 2.995732 9.358191 11.114877 -0.52763274
## X145 -7.338538 4.260413 3.218876 8.542148 10.165525 -0.89159812
## X146 -5.298317 3.695039 3.610918 9.358191 13.099669 -0.17435339
## X147 -6.032287 5.478731 3.258097 9.454406 11.856406 -0.15082289
## X148 -5.051457 3.867347 3.295837 8.858503 14.733201 -0.52763274
## X149 -6.645391 3.924249 3.135494 9.828139 11.416408 -0.71334989
## X152 -5.051457 5.478731 3.688879 8.542148 14.370706 0.00000000
## X153 -5.626821 4.749337 3.295837 10.528568 13.231546 -0.56211892
## X154 -5.914504 3.980894 2.772589 8.542148 10.489996 -0.86750057
## X155 -5.776353 4.260413 3.295837 8.433621 9.313708 -0.79850770
## X156 -5.472671 4.037285 3.496508 10.528568 14.000000 -0.41551544
## X157 -6.119298 4.370576 3.295837 8.858503 14.733201 -0.67334455
## X158 -6.319969 3.695039 2.890372 10.444102 10.489996 -0.79850770
## X159 -6.571283 3.637051 3.091042 10.444102 9.661904 -0.91629073
## X160 -5.991465 6.225224 3.496508 11.559326 12.142136 -0.26136476
## X161 -5.991465 4.149327 3.091042 11.926999 10.489996 -0.43078292
## X162 -7.308233 3.810182 3.784190 10.528568 10.806248 -0.38566248
## X163 -6.725434 4.093428 2.944439 10.939092 8.583005 -0.94160854
## X165 -6.645391 3.867347 3.367296 8.961066 11.856406 -0.40047757
## X166 -6.571283 4.315608 2.772589 9.736307 9.313708 -0.75502258
## X167 -6.265901 5.222195 4.007333 12.419275 11.564660 -0.26136476
## X168 -5.843045 3.924249 3.135494 10.272644 11.856406 -0.38566248
## X169 -6.725434 4.149327 3.135494 9.549474 9.489125 -0.86750057
## X170 -5.426151 4.315608 2.833213 9.062271 9.832160 -0.75502258
## X171 -5.572754 4.425322 3.806662 11.176634 11.266499 -0.59783700
## X172 -6.437752 3.695039 3.555348 7.982407 9.135529 -1.10866262
## X174 -5.654992 4.370576 3.178054 11.018969 14.000000 -0.51082562
## X175 -5.099467 3.810182 3.526361 8.754531 14.613248 -0.24846136
## X176 -5.952244 3.578777 3.367296 9.549474 11.564660 -0.69314718
## X177 -6.437752 3.282892 3.891820 11.018969 11.856406 -0.54472718
## X178 -6.074846 2.791992 2.772589 8.961066 8.392305 -1.30933332
## X179 -5.521461 4.204987 3.610918 10.185606 14.492423 -0.26136476
## X180 -6.319969 3.924249 3.258097 9.736307 11.266499 0.09531018
## X181 -6.214608 2.275226 3.044522 11.408143 11.564660 -0.91629073
## X182 -6.074846 3.924249 3.526361 8.323453 11.564660 -0.75502258
## X183 -5.683980 2.005028 3.295837 8.097921 11.266499 -0.86750057
## X184 -5.809143 3.752748 3.761200 12.822626 12.560220 -0.63487827
## X185 -6.645391 3.461346 3.401197 11.018969 12.696938 -0.56211892
## X186 -6.437752 3.867347 2.995732 10.939092 9.489125 -0.82098055
## X189 -6.571283 4.479850 3.367296 7.113891 9.661904 -0.52763274
## X190 -5.991465 3.867347 3.178054 11.634012 12.282857 -0.35667494
## X191 -5.572754 3.282892 3.401197 10.358802 11.114877 -0.69314718
## X192 -6.265901 4.037285 3.713572 10.444102 13.362291 -0.15082289
## X193 -6.119298 4.961345 3.295837 10.444102 15.320508 -0.32850407
## X194 -6.214608 3.402176 3.044522 9.828139 12.000000 -0.75502258
## X195 -6.074846 5.731246 3.988984 11.559326 12.000000 -0.24846136
## X197 -5.809143 5.630705 3.367296 10.444102 11.266499 -0.24846136
## X198 -6.502290 3.162299 3.044522 9.454406 9.489125 -0.91629073
## X200 -6.917806 5.222195 3.295837 8.754531 10.806248 -0.59783700
## X201 -5.776353 2.916923 2.772589 10.695079 12.560220 -0.77652879
## X202 -6.265901 3.867347 2.890372 7.500000 9.832160 -1.13943428
## X205 -5.809143 4.315608 3.135494 10.695079 11.114877 -0.73396918
## X208 -5.572754 4.315608 3.178054 10.358802 12.282857 -0.54472718
## X210 -6.214608 4.749337 3.367296 12.141017 13.099669 -0.28768207
## X212 -6.214608 3.924249 3.091042 9.828139 10.489996 -0.89159812
## X213 -6.214608 4.149327 3.091042 10.008788 9.661904 -0.46203546
## X214 -5.654992 3.101492 3.433987 10.008788 16.547237 -0.32850407
## X215 -6.725434 3.695039 3.465736 8.542148 9.832160 -0.75502258
## X216 -5.203007 3.222763 2.944439 8.961066 10.649111 -1.07880966
## X218 -4.906275 3.461346 2.944439 10.185606 13.362291 -0.82098055
## X219 -6.214608 3.162299 2.772589 9.736307 10.806248 -1.02165125
## X220 -6.319969 4.260413 3.583519 8.754531 10.489996 -0.96758403
## X223 -6.214608 4.149327 3.850148 8.754531 11.564660 -0.59783700
## X224 -6.032287 4.204987 3.555348 7.623847 11.266499 -0.89159812
## X225 -6.437752 5.325310 4.060443 9.162164 12.000000 -0.26136476
## X226 -5.991465 4.642159 3.891820 10.528568 13.231546 0.09531018
## X227 -4.645992 3.461346 3.526361 8.858503 11.711309 -0.63487827
## X228 -6.645391 3.342694 3.295837 7.982407 9.661904 -0.94160854
## X229 -5.991465 3.402176 2.833213 10.097662 9.489125 -1.07880966
## X230 -5.809143 2.791992 3.258097 9.454406 12.282857 -0.26136476
## X231 -5.809143 3.461346 3.367296 13.827158 1.741657 -0.65392647
## X232 -5.809143 4.149327 3.044522 11.634012 11.856406 -0.73396918
## X233 -6.214608 2.978813 2.995732 10.358802 10.649111 -0.96758403
## X234 -5.546779 3.578777 3.367296 8.754531 10.328828 -0.79850770
## X236 -7.169120 3.752748 2.995732 9.162164 8.954451 -0.84397007
## X237 -5.496768 5.118391 2.564949 7.500000 11.856406 -0.67334455
## X239 -7.293418 1.725381 3.044522 7.500000 9.661904 -0.99425227
## X240 -6.571283 3.402176 3.401197 7.745463 10.165525 -0.96758403
## X241 -5.744604 4.749337 3.496508 9.549474 10.000000 -0.65392647
## X242 -4.866535 2.728930 3.135494 9.643429 12.422205 -0.44628710
## X243 -5.654992 4.315608 2.772589 9.260790 11.711309 -1.07880966
## X244 -6.377127 3.461346 3.258097 10.272644 12.000000 -0.30110509
## X245 -6.502290 2.341451 2.397895 10.185606 9.135529 -1.38629436
## X246 -5.259097 3.282892 3.401197 7.745463 13.620499 -0.51082562
## X247 -6.917806 3.222763 2.944439 8.097921 9.832160 -1.07880966
## X249 -6.502290 5.731246 3.891820 10.444102 11.114877 -0.03045921
## X250 -5.713833 3.461346 3.135494 9.918956 11.856406 -0.51082562
## X251 -6.725434 2.854653 2.944439 10.272644 9.832160 -0.82098055
## X253 -5.496768 3.810182 3.295837 9.828139 15.888544 -0.41551544
## X254 -5.878136 4.425322 4.276666 10.358802 12.966630 0.26236426
## X255 -5.744604 4.802628 3.663562 11.254454 12.832397 -0.22314355
## X256 -7.402052 3.980894 3.178054 9.358191 8.954451 -1.04982212
## X257 -6.074846 3.342694 2.944439 10.185606 9.135529 -1.13943428
## X258 -6.571283 4.370576 3.332205 10.695079 13.099669 -0.34249031
## X260 -4.906275 4.425322 3.401197 10.358802 12.142136 -0.69314718
## X261 -6.645391 3.637051 2.944439 10.272644 8.954451 -0.91629073
## X262 -5.914504 3.101492 3.135494 10.185606 9.135529 -1.10866262
## X263 -5.776353 3.980894 3.891820 11.854592 14.492423 -0.47803580
## X264 -5.599422 4.479850 3.583519 9.260790 14.248077 -0.38566248
## X265 -5.744604 3.101492 3.401197 10.777165 12.696938 -0.75502258
## X267 -6.032287 5.013876 3.258097 11.018969 13.099669 -0.02020271
## X268 -6.319969 4.479850 3.637586 10.272644 10.165525 -0.34249031
## X269 -7.035589 2.472433 3.044522 6.842982 9.489125 -0.75502258
## X270 -6.377127 3.578777 3.951244 11.708110 10.649111 -0.19845094
## X271 -5.776353 4.093428 3.637586 13.273934 17.899749 -0.17435339
## X272 -5.744604 3.980894 3.401197 8.323453 10.328828 -0.77652879
## X273 -5.952244 3.980894 3.555348 9.358191 12.560220 -0.22314355
## X274 -6.074846 3.461346 3.258097 12.350442 11.416408 -0.57981850
## X275 -5.776353 4.695848 3.713572 7.982407 13.231546 -0.26136476
## X277 -5.020686 4.749337 3.135494 10.185606 12.142136 -0.57981850
## X278 -5.403678 3.637051 3.637586 8.649098 12.560220 -0.41551544
## X279 -6.377127 3.924249 3.258097 8.961066 10.328828 -0.61618614
## X281 -5.914504 5.170380 3.367296 12.555529 12.282857 -0.44628710
## X282 -5.298317 2.791992 2.890372 6.842982 13.491933 -0.82098055
## X283 -6.265901 5.170380 3.663562 10.858497 15.088007 -0.10536052
## X287 -6.119298 4.370576 3.433987 9.549474 11.266499 -0.40047757
## X289 -6.032287 2.665456 3.178054 7.982407 9.489125 -1.07880966
## X290 -6.319969 2.728930 3.135494 9.549474 11.114877 -0.71334989
## X291 -5.744604 4.204987 3.465736 10.695079 14.370706 -0.17435339
## X292 -5.878136 4.749337 3.850148 9.454406 13.362291 -0.26136476
## X294 -5.683980 2.854653 2.890372 7.373808 10.165525 -1.02165125
## X297 -6.571283 3.342694 2.944439 9.549474 10.806248 -0.89159812
## X298 -5.132803 2.341451 2.890372 10.358802 12.282857 -0.96758403
## X299 -7.338538 3.578777 3.465736 9.828139 10.961481 -0.54472718
## X301 -5.521461 2.916923 3.091042 10.528568 9.832160 -0.99425227
## X302 -5.843045 2.073409 3.178054 7.373808 9.489125 -1.66073121
## X303 -5.203007 3.578777 3.401197 11.559326 11.564660 -0.82098055
## X304 -6.502290 5.478731 3.583519 11.559326 11.564660 -0.19845094
## X305 -5.952244 5.066223 3.401197 11.998865 14.970563 -0.40047757
## X306 -5.654992 4.037285 4.007333 10.008788 13.874508 -0.15082289
## X307 -5.318520 5.118391 3.850148 7.982407 16.110770 -0.16251893
## X308 -5.360193 4.855724 3.465736 8.649098 12.142136 -0.56211892
## X311 -6.165818 3.461346 3.178054 9.918956 11.114877 -0.67334455
## X312 -6.119298 4.425322 3.496508 11.708110 12.422205 -0.46203546
## X313 -5.776353 3.637051 3.295837 11.176634 12.000000 -0.82098055
## X314 -6.725434 4.370576 2.944439 10.444102 11.416408 -0.77652879
## X315 -5.149897 3.924249 3.401197 7.113891 12.142136 -0.67334455
## X316 -6.571283 3.695039 2.890372 10.008788 11.711309 -0.52763274
## X317 -6.214608 3.402176 3.258097 8.649098 12.000000 -0.37106368
## X320 -5.184989 3.282892 3.044522 9.454406 11.856406 -0.91629073
## X321 -5.259097 2.854653 2.397895 8.433621 11.114877 -1.02165125
## X322 -5.099467 4.749337 3.663562 11.408143 10.961481 -0.40047757
## X323 -6.165818 3.222763 3.401197 10.008788 12.696938 -0.46203546
## X324 -6.725434 3.637051 3.465736 8.211578 11.416408 -0.71334989
## X325 -6.032287 3.222763 3.465736 10.008788 14.733201 -0.47803580
## X326 -7.182192 3.637051 3.401197 9.549474 11.711309 -0.40047757
## X327 -5.878136 3.752748 3.258097 9.643429 12.142136 -0.27443685
## X329 -6.214608 4.037285 3.496508 9.918956 10.961481 -0.61618614
## X330 -5.132803 3.402176 2.833213 8.754531 10.961481 -0.79850770
## X331 -6.645391 3.752748 3.044522 10.358802 10.165525 -1.17118298
## X332 -5.403678 3.222763 2.944439 10.777165 12.560220 -1.02165125
## X333 -5.952244 5.273838 3.713572 11.408143 11.564660 -0.21072103
## TRAIL_R3 TTR_prealbumin Tamm_Horsfall_Protein_THP Thrombomodulin
## X1 -0.18290044 2.944439 -3.095810 -1.3405665
## X2 -0.50074709 2.833213 -3.111190 -1.6752524
## X3 -0.92403445 2.944439 -3.166721 -1.5342758
## X5 -0.85825911 3.044522 -3.038017 -1.2107086
## X6 -0.73800921 3.044522 -3.125574 -1.4516659
## X7 -0.62997381 2.890372 -3.133732 -1.6752524
## X8 -0.56347899 2.708050 -3.056473 -1.2107086
## X9 -0.75712204 2.772589 -3.128881 -1.4130880
## X11 -0.37116408 2.833213 -3.111190 -1.5342758
## X12 -0.68264012 2.833213 -3.126226 -1.2733760
## X14 -0.54746226 2.890372 -3.100676 -1.2733760
## X16 -0.48559774 2.890372 -3.128881 -1.3761017
## X17 0.00000000 2.833213 -3.074400 -1.4130880
## X18 -0.75712204 2.833213 -3.123014 -2.0376622
## X19 -0.41274719 2.890372 -3.111190 -1.7844998
## X20 -0.85825911 2.772589 -3.123014 -1.1238408
## X21 0.26936976 3.044522 -3.156593 -1.7280531
## X22 -0.20634242 2.639057 -3.139636 -1.5787229
## X23 -0.56347899 3.091042 -3.132318 -1.6752524
## X24 -0.25465110 2.639057 -3.100676 -1.3405665
## X25 -0.70078093 2.772589 -3.123014 -1.5787229
## X26 -0.37116408 2.639057 -3.116914 -1.4516659
## X28 -0.70078093 2.772589 -3.125574 -1.5787229
## X29 -0.83723396 2.708050 -3.123014 -1.6256074
## X30 -0.94693458 3.091042 -3.105791 -1.7844998
## X31 -0.62997381 2.944439 -3.116914 -1.4130880
## X34 -0.13734056 3.091042 -3.111190 -1.2415199
## X35 -0.64724718 2.833213 -3.116914 -1.2733760
## X36 -0.68264012 2.833213 -3.040908 -1.1238408
## X37 -0.34425042 2.833213 -3.095810 -1.5787229
## X38 -0.56347899 2.772589 -3.126226 -1.3405665
## X39 -0.57973042 2.772589 -3.144357 -1.4516659
## X40 -0.47064906 2.772589 -3.135170 -1.3761017
## X41 -0.47064906 2.944439 -3.050006 -1.3405665
## X42 -0.73800921 2.833213 -3.095810 -1.4516659
## X43 -0.48559774 2.890372 -3.125574 -1.6752524
## X44 -0.64724718 2.708050 -3.100676 -1.2107086
## X45 -0.21823750 2.890372 -3.078354 -1.5342758
## X46 -0.57973042 2.772589 -3.158511 -1.4516659
## X47 -0.64724718 2.890372 -3.135170 -1.5787229
## X48 -0.13734056 2.772589 -3.123014 -1.5787229
## X50 0.00000000 2.772589 -3.105791 -1.1808680
## X51 -0.73800921 2.639057 -3.074400 -1.6752524
## X53 -0.53167272 2.944439 -3.095810 -1.4516659
## X55 -0.47064906 2.708050 -3.138121 -1.5342758
## X56 -0.37116408 2.944439 -3.074400 -1.1238408
## X57 -0.27956244 2.772589 -3.116914 -1.5787229
## X59 -0.56347899 2.995732 -3.095810 -1.3761017
## X60 -0.53167272 2.995732 -3.095810 -1.9461072
## X61 -0.79641472 2.944439 -3.133732 -1.8452133
## X62 -1.09654116 3.091042 -3.095810 -1.8708654
## X63 -0.33102365 2.995732 -3.116914 -1.4130880
## X64 -0.44133043 2.708050 -3.070582 -1.2733760
## X65 -0.68264012 2.833213 -3.154723 -1.7280531
## X67 -0.56347899 2.890372 -3.205541 -1.4130880
## X68 -0.31794508 2.708050 -3.105791 -1.2733760
## X69 -1.09654116 2.833213 -3.143551 -1.9248483
## X70 -0.39871863 2.772589 -3.111190 -1.5342758
## X71 -0.61296931 3.178054 -3.147663 -1.4919984
## X72 -0.21823750 2.833213 -3.074400 -1.3761017
## X73 -0.30501103 2.639057 -3.158511 -1.3063602
## X74 -0.90163769 2.944439 -3.086722 -1.6752524
## X75 -0.77658561 2.833213 -3.111190 -1.3063602
## X76 -0.30501103 2.890372 -3.111190 -1.2733760
## X77 -0.31794508 2.708050 -3.111190 -1.2107086
## X78 -0.10425819 2.833213 -3.082457 -1.3405665
## X80 -0.47064906 3.135494 -3.133732 -1.6256074
## X81 -0.62997381 2.833213 -3.126226 -1.3063602
## X82 -0.68264012 2.708050 -3.095810 -1.6256074
## X83 -0.44133043 2.890372 -3.111190 -1.2415199
## X84 -0.54746226 2.772589 -3.126226 -1.3405665
## X85 -0.37116408 2.890372 -3.078354 -1.3063602
## X86 -0.30501103 2.944439 -3.070582 -1.2415199
## X88 -0.17134851 2.772589 -3.125574 -1.3405665
## X90 -0.81662520 2.833213 -3.146824 -2.0295903
## X93 -0.44133043 2.890372 -3.086722 -1.4919984
## X94 0.00000000 2.995732 -3.086722 -0.8166252
## X95 -0.54746226 2.639057 -3.091166 -1.4130880
## X96 -0.54746226 2.772589 -3.168929 -1.5787229
## X97 -0.71923319 2.772589 -3.139636 -1.5787229
## X98 -0.92403445 2.995732 -3.136633 -1.9109957
## X99 -0.57973042 2.890372 -3.171207 -1.6256074
## X100 -0.48559774 2.639057 -3.100676 -1.3063602
## X103 -0.38485910 2.772589 -3.144357 -1.4516659
## X104 -0.45589516 2.833213 -3.105791 -1.5787229
## X105 -0.75712204 2.708050 -3.105791 -1.5787229
## X107 -0.47064906 2.944439 -3.116914 -1.6752524
## X108 -0.61296931 2.944439 -3.053196 -1.4919984
## X109 -1.21070858 2.995732 -3.154723 -1.5787229
## X110 -0.75712204 2.944439 -3.166721 -1.9109957
## X111 -0.75712204 2.708050 -3.074400 -1.6752524
## X112 -0.27956244 3.091042 -3.091166 -1.4516659
## X113 -0.42694948 2.890372 -3.091166 -1.3405665
## X114 -0.83723396 2.890372 -3.139636 -1.6752524
## X115 -0.42694948 3.044522 -3.059843 -1.0965412
## X117 -0.42694948 2.708050 -3.100676 -1.7280531
## X118 -0.42694948 2.944439 -3.123014 -1.4516659
## X121 -0.13734056 2.772589 -3.086722 -1.2415199
## X123 -0.99435191 2.833213 -3.143551 -1.8708654
## X124 -0.20634242 2.708050 -3.095810 -1.4919984
## X126 -0.38485910 2.564949 -3.123014 -1.4130880
## X128 -0.27956244 2.639057 -3.100676 -1.1808680
## X129 -0.70078093 2.772589 -3.153804 -1.4130880
## X130 -0.39871863 2.833213 -3.111190 -1.3761017
## X131 -0.87972006 2.772589 -3.123647 -1.7280531
## X132 -0.62997381 2.890372 -3.116914 -1.4919984
## X133 -0.92403445 3.044522 -3.053196 -1.4130880
## X134 -0.31794508 2.772589 -3.136633 -1.3405665
## X135 -0.53167272 2.772589 -3.086722 -1.7844998
## X136 -0.38485910 2.639057 -3.100676 -1.4516659
## X137 -1.04412698 2.639057 -3.171207 -1.7280531
## X139 -0.47064906 2.995732 -3.136633 -1.7844998
## X140 -0.48559774 2.833213 -3.168929 -1.5342758
## X141 -0.34425042 2.639057 -3.086722 -1.3063602
## X143 -0.61296931 3.258097 -3.100676 -1.4919984
## X144 -0.64724718 2.708050 -3.145993 -1.6752524
## X145 -0.39871863 2.944439 -3.166721 -1.7844998
## X146 -0.53167272 2.772589 -3.074400 -1.3063602
## X147 -0.47064906 2.564949 -3.149369 -1.3761017
## X148 -0.94693458 3.044522 -3.100676 -1.2415199
## X149 -0.97036428 2.772589 -3.176003 -1.5787229
## X152 -0.18290044 2.772589 -3.111190 -1.2415199
## X153 -0.41274719 2.833213 -3.086722 -1.0441270
## X154 -0.85825911 2.833213 -3.138121 -1.9461072
## X155 -0.75712204 2.890372 -3.091166 -1.7280531
## X156 -0.61296931 2.833213 -3.100676 -1.2107086
## X157 -0.42694948 2.944439 -3.116914 -1.4919984
## X158 -0.81662520 2.890372 -3.105791 -1.7280531
## X159 -0.75712204 2.890372 -3.095810 -1.3761017
## X160 -0.59622443 2.944439 -3.105791 -1.3405665
## X161 -0.30501103 2.890372 -3.138875 -1.3405665
## X162 -0.42694948 2.833213 -3.126882 -1.6256074
## X163 -0.75712204 2.995732 -3.139636 -1.6752524
## X165 -0.44133043 2.833213 -3.136633 -1.7280531
## X166 -0.79641472 2.944439 -3.149369 -1.6256074
## X167 -0.30501103 2.708050 -3.111190 -1.5342758
## X168 -0.62997381 2.995732 -3.123014 -1.3761017
## X169 -0.51610326 2.890372 -3.091166 -1.6256074
## X170 -0.47064906 3.044522 -3.105791 -1.5342758
## X171 -0.38485910 2.564949 -3.100676 -1.0189283
## X172 -0.62997381 2.890372 -3.140405 -1.7844998
## X174 -0.51610326 3.044522 -3.111190 -1.1238408
## X175 -0.31794508 2.772589 -3.074400 -1.4919984
## X176 -0.56347899 2.772589 -3.095810 -1.3063602
## X177 -0.68264012 2.708050 -3.095810 -1.7280531
## X178 -0.51610326 2.833213 -3.141963 -1.4130880
## X179 -0.54746226 2.833213 -3.100676 -1.0965412
## X180 -0.21823750 2.833213 -3.145993 -1.5342758
## X181 -0.97036428 2.708050 -3.126882 -1.9041616
## X182 -0.61296931 2.772589 -3.105791 -1.7280531
## X183 -0.68264012 2.995732 -3.116914 -1.5342758
## X184 -0.37116408 2.708050 -3.136633 -1.4130880
## X185 -0.15990607 2.833213 -3.056473 -1.3405665
## X186 -0.30501103 2.890372 -3.125574 -1.6256074
## X189 -0.68264012 2.639057 -3.160479 -1.7280531
## X190 -0.47064906 2.944439 -3.100676 -1.5342758
## X191 -0.68264012 2.833213 -3.126882 -1.6256074
## X192 -0.38485910 2.708050 -3.123647 -1.2733760
## X193 -0.47064906 2.772589 -3.111190 -1.3761017
## X194 -0.70078093 3.044522 -3.139636 -1.4130880
## X195 -0.47064906 2.564949 -3.091166 -1.3405665
## X197 -0.06149412 2.639057 -3.133732 -1.3063602
## X198 -0.79641472 2.833213 -3.082457 -1.6752524
## X200 -0.79641472 2.772589 -3.095810 -1.8452133
## X201 -0.38485910 2.772589 -3.155652 -1.4919984
## X202 -0.79641472 2.995732 -3.166721 -1.9754065
## X205 -0.59622443 2.708050 -3.205541 -1.2733760
## X208 -0.62997381 2.833213 -3.082457 -1.4516659
## X210 -0.21823750 2.772589 -3.139636 -1.2733760
## X212 -0.71923319 2.890372 -3.074400 -1.6256074
## X213 -0.41274719 2.833213 -3.123014 -1.4516659
## X214 -0.71923319 2.833213 -3.147663 -1.7280531
## X215 -0.68264012 2.890372 -3.125574 -1.8452133
## X216 -0.38485910 2.890372 -3.066890 -1.5342758
## X218 -0.51610326 2.772589 -3.082457 -1.5342758
## X219 -0.68264012 2.944439 -3.171207 -1.5787229
## X220 -0.71923319 2.890372 -3.145993 -1.3405665
## X223 -0.62997381 3.044522 -3.091166 -1.6256074
## X224 -0.70078093 3.044522 -3.130927 -1.8708654
## X225 -0.34425042 2.833213 -3.116914 -1.5342758
## X226 -0.41274719 2.833213 -3.116914 -1.9389551
## X227 -0.50074709 3.135494 -3.086722 -1.3405665
## X228 -0.71923319 2.879892 -3.143551 -1.9606157
## X229 -0.57973042 2.944439 -3.130927 -1.7844998
## X230 -0.42694948 2.944439 -3.123014 -1.4130880
## X231 -0.51610326 2.708050 -3.100676 -1.3761017
## X232 -0.42694948 2.944439 -3.123014 -1.4919984
## X233 -0.90163769 2.890372 -3.129558 -1.6752524
## X234 -0.56347899 2.890372 -3.053196 -1.3761017
## X236 -0.56347899 2.639057 -3.160479 -1.6256074
## X237 -0.51610326 2.890372 -3.082457 -1.4130880
## X239 -0.77658561 2.708050 -3.140405 -1.9248483
## X240 -0.83723396 3.091042 -3.166721 -1.9248483
## X241 -0.54746226 2.639057 -3.095810 -1.5342758
## X242 -0.35762924 3.178054 -2.994538 -1.2415199
## X243 -0.75712204 2.890372 -3.105791 -1.4130880
## X244 -0.41274719 2.772589 -3.116914 -1.2733760
## X245 -0.77658561 2.890372 -3.205541 -1.9041616
## X246 -0.41274719 2.995732 -3.111190 -1.5787229
## X247 -0.70078093 2.564949 -3.155652 -1.6256074
## X249 -0.15990607 2.564949 -3.152897 -1.6256074
## X250 -0.50074709 3.044522 -3.123014 -1.8452133
## X251 -0.68264012 2.944439 -3.123014 -1.4919984
## X253 -0.42694948 2.833213 -3.095810 -1.1519318
## X254 0.18568645 2.995732 -3.123014 -1.2733760
## X255 0.00000000 2.708050 -3.038017 -1.1519318
## X256 -0.64724718 2.833213 -3.123014 -1.5342758
## X257 -0.64724718 2.772589 -3.145993 -1.6752524
## X258 -0.34425042 2.772589 -3.151113 -1.4130880
## X260 -0.41274719 2.890372 -3.105791 -1.7844998
## X261 -0.71923319 2.772589 -3.116914 -1.5342758
## X262 -0.90163769 2.833213 -3.091166 -1.7844998
## X263 -0.47064906 3.044522 -3.086722 -1.6256074
## X264 -0.06149412 2.890372 -3.063313 -0.8582591
## X265 -0.81662520 2.890372 -3.126882 -1.8452133
## X267 -0.18290044 2.708050 -3.100676 -1.3405665
## X268 -0.44133043 2.708050 -3.135898 -1.5787229
## X269 -0.34425042 3.178054 -3.136633 -1.9754065
## X270 -0.41274719 2.944439 -3.142753 -1.5787229
## X271 -0.20634242 2.944439 -3.095810 -1.3063602
## X272 -0.53167272 2.833213 -3.082457 -1.4130880
## X273 -0.38485910 2.564949 -3.105791 -1.3405665
## X274 -0.38485910 2.833213 -3.105791 -1.9389551
## X275 -0.85825911 2.772589 -3.105791 -1.4919984
## X277 -0.48559774 2.772589 -3.116914 -1.4516659
## X278 -0.53167272 2.890372 -3.128210 -1.8452133
## X279 -0.57973042 2.708050 -3.123014 -1.6256074
## X281 -0.21823750 2.772589 -3.100676 -1.6752524
## X282 -0.73800921 2.890372 -3.158511 -1.4130880
## X283 -0.29221795 2.772589 -3.126882 -1.0189283
## X287 -0.42694948 2.833213 -3.126226 -1.5342758
## X289 -0.92403445 3.044522 -3.116914 -1.7844998
## X290 -0.42694948 2.639057 -3.147663 -1.6752524
## X291 -0.35762924 2.890372 -3.149369 -1.5787229
## X292 -0.37116408 3.332205 -3.123014 -1.3063602
## X294 -0.50074709 3.044522 -3.136633 -1.9248483
## X297 -0.64724718 2.890372 -3.091166 -1.4919984
## X298 -0.77658561 3.044522 -3.176003 -1.5342758
## X299 -0.37116408 2.944439 -3.152897 -1.5787229
## X301 -0.71923319 3.044522 -3.145993 -1.5787229
## X302 -0.87972006 2.833213 -3.100676 -1.4919984
## X303 -0.70078093 2.890372 -3.095810 -1.5787229
## X304 -0.29221795 2.890372 -3.138875 -1.5342758
## X305 -0.54746226 2.833213 -3.105791 -1.4919984
## X306 -0.48559774 2.890372 -3.105791 -1.3063602
## X307 -0.21823750 2.833213 -3.095810 -1.2415199
## X308 -0.56347899 2.995732 -3.086722 -1.6256074
## X311 -0.35762924 2.833213 -3.086722 -1.2733760
## X312 -0.21823750 2.890372 -3.091166 -1.2733760
## X313 -0.83723396 3.044522 -3.205541 -1.5787229
## X314 -0.66479918 2.484907 -3.135898 -1.7280531
## X315 -0.56347899 3.091042 -3.050006 -1.5342758
## X316 -1.15193183 2.772589 -3.166721 -1.4516659
## X317 -0.59622443 2.890372 -3.205541 -1.3761017
## X320 -0.75712204 3.091042 -3.086722 -1.1808680
## X321 -1.01892829 2.708050 -3.095810 -1.5787229
## X322 -0.37116408 2.772589 -3.056473 -0.9943519
## X323 -0.31794508 2.833213 -3.123014 -1.3405665
## X324 -0.59622443 2.708050 -3.143551 -1.5787229
## X325 -0.68264012 2.890372 -3.105791 -1.7844998
## X326 -0.42694948 2.890372 -3.123647 -1.6256074
## X327 -0.41274719 2.995732 -3.149369 -1.2107086
## X329 -0.68264012 2.995732 -3.111190 -1.4919984
## X330 -0.77658561 3.135494 -3.035191 -1.2107086
## X331 -1.01892829 2.833213 -3.095810 -1.5787229
## X332 -0.94693458 2.890372 -3.091166 -1.4516659
## X333 -0.38485910 2.772589 -3.056473 -1.5787229
## Thrombopoietin Thymus_Expressed_Chemokine_TECK Thyroid_Stimulating_Hormone
## X1 -0.1026334 4.149327 -3.863233
## X2 -0.6733501 3.810182 -4.828314
## X3 -0.9229670 2.791992 -4.990833
## X5 0.0976177 4.534163 -4.645992
## X6 -1.0000000 4.534163 -4.422849
## X7 -0.3386752 3.342694 -3.963316
## X8 -0.6583592 4.037285 -4.017384
## X9 -0.8864471 3.637051 -4.605170
## X11 -0.8000000 4.908629 -3.442019
## X12 -0.5577795 3.637051 -4.605170
## X14 -1.0834849 4.534163 -3.816713
## X16 -0.8000000 4.093428 -4.892852
## X17 -0.6885123 5.273838 -4.422849
## X18 -1.0619168 2.407182 -5.005648
## X19 -0.9801961 4.260413 -4.509860
## X20 -1.2254033 3.810182 -4.422849
## X21 -1.1282202 4.908629 -4.509860
## X22 -0.5857864 3.578777 -4.199705
## X23 -1.0834849 3.810182 -5.381699
## X24 -0.7193752 4.534163 -5.449140
## X25 -1.0619168 2.472433 -4.422849
## X26 -0.8000000 4.149327 -4.615221
## X28 -0.8000000 3.282892 -5.449140
## X29 -0.5577795 3.578777 -4.074542
## X30 -0.6435340 2.407182 -4.342806
## X31 -0.4900331 2.854653 -4.509860
## X34 -1.5395654 4.093428 -4.605170
## X35 -0.7038519 4.093428 -3.863233
## X36 -0.4508067 4.479850 -5.298317
## X37 -0.6885123 4.149327 -4.879607
## X38 -0.7038519 4.093428 -5.298317
## X39 -0.7038519 4.315608 -5.496768
## X40 -0.9607695 1.936058 -4.342806
## X41 -0.8000000 3.637051 -4.733004
## X42 -1.1753789 4.093428 -4.605170
## X43 -0.7350889 3.342694 -5.005648
## X44 -0.7038519 2.791992 -4.767689
## X45 -0.7193752 4.908629 -3.649659
## X46 -0.9607695 4.315608 -5.167289
## X47 -1.0202041 3.867347 -5.599422
## X48 -0.3629294 3.752748 -4.509860
## X50 -0.8338096 4.093428 -4.135167
## X51 -0.7510004 3.810182 -4.017384
## X53 -0.7834475 3.810182 -4.990833
## X55 -0.9607695 2.791992 -4.699481
## X56 -0.8864471 2.601557 -5.599422
## X57 -0.4637709 6.225224 -5.083206
## X59 -1.0202041 3.101492 -4.509860
## X60 -0.7834475 1.796259 -3.912023
## X61 -0.8510875 2.854653 -3.611918
## X62 -0.2111456 2.854653 -4.645992
## X63 -0.8864471 4.479850 -4.342806
## X64 -0.8864471 4.479850 -6.189915
## X65 -0.9607695 3.810182 -4.635629
## X67 -1.1753789 4.855724 -3.816713
## X68 -0.5303062 6.225224 -5.083206
## X69 -0.6733501 2.854653 -5.259097
## X70 -0.8510875 2.791992 -4.268698
## X71 -0.9607695 3.810182 -4.422849
## X72 -0.3147700 4.749337 -4.135167
## X73 -0.8686292 4.961345 -3.611918
## X74 -1.0619168 3.752748 -4.199705
## X75 -0.6288691 3.810182 -3.611918
## X76 -0.8864471 4.479850 -3.442019
## X77 -0.6583592 5.325310 -3.473768
## X78 -0.7193752 6.225224 -6.189915
## X80 -0.7350889 3.342694 -2.673649
## X81 -0.6288691 3.637051 -4.342806
## X82 -0.8000000 3.282892 -5.221356
## X83 -0.6583592 4.908629 -4.733004
## X84 -0.8864471 3.637051 -4.605170
## X85 -0.6288691 4.479850 -4.509860
## X86 -0.2679492 4.479850 -3.816713
## X88 -0.8510875 4.149327 -3.442019
## X90 -0.9045549 3.040333 -3.688879
## X93 -0.8510875 4.037285 -4.342806
## X94 -0.7510004 5.222195 -6.189915
## X95 -0.5577795 3.752748 -5.259097
## X96 -0.8000000 3.980894 -3.963316
## X97 -1.1282202 4.149327 -4.615221
## X98 -0.7350889 1.508487 -4.645992
## X99 -0.6288691 3.810182 -5.167289
## X100 -1.1753789 3.101492 -6.189915
## X103 -0.5717143 4.534163 -2.813411
## X104 -0.6583592 4.037285 -4.605170
## X105 -0.7834475 3.810182 -5.381699
## X107 -0.4379501 3.752748 -3.863233
## X108 -0.2911993 3.342694 -4.475594
## X109 -0.9607695 2.208489 -4.268698
## X110 -0.8510875 2.854653 -5.259097
## X111 -0.6583592 3.040333 -3.688879
## X112 -0.3147700 4.149327 -4.268698
## X113 -0.4251984 4.149327 -4.199705
## X114 -1.2788897 2.791992 -3.324236
## X115 -1.0202041 3.637051 -6.189915
## X117 -0.5577795 4.315608 -4.422849
## X118 -0.8864471 4.149327 -4.615221
## X121 -0.8338096 3.637051 -3.649659
## X123 -0.6733501 2.854653 -4.645992
## X124 -0.7038519 3.040333 -4.803621
## X126 -0.7038519 3.867347 -6.189915
## X128 -0.8338096 4.093428 -3.963316
## X129 -1.3071797 3.101492 -6.189915
## X130 -0.8864471 4.908629 -4.733004
## X131 -0.7038519 3.578777 -4.342806
## X132 -0.5577795 3.752748 -3.963316
## X133 -0.6583592 4.149327 -4.268698
## X134 -0.8864471 6.225224 -3.218876
## X135 -0.7350889 3.342694 -3.649659
## X136 -0.5857864 3.578777 -4.017384
## X137 -1.0202041 2.791992 -4.990833
## X139 -1.0000000 3.282892 -4.199705
## X140 -0.9416995 4.149327 -1.714798
## X141 -1.0000000 4.149327 -3.296837
## X143 -0.5577795 3.752748 -4.268698
## X144 -1.0000000 3.752748 -4.947660
## X145 -0.7350889 2.854653 -5.259097
## X146 -0.6583592 3.980894 -4.422849
## X147 -0.8000000 3.752748 -3.611918
## X148 -0.2564404 4.479850 -4.733004
## X149 -0.7038519 3.342694 -5.381699
## X152 -0.2679492 5.580204 -3.218876
## X153 -0.7193752 4.149327 -4.879607
## X154 -1.0834849 2.537220 -4.342806
## X155 -0.7350889 3.342694 -4.803621
## X156 -0.6733501 4.315608 -5.626821
## X157 -0.4125492 3.752748 -4.422849
## X158 -1.0834849 3.342694 -5.381699
## X159 -0.8864471 3.637051 -4.605170
## X160 -0.5717143 4.315608 -4.605170
## X161 -1.1753789 4.479850 -4.509860
## X162 -0.6583592 3.578777 -6.189915
## X163 -0.8510875 4.479850 -5.083206
## X165 -1.0000000 3.040333 -5.035953
## X166 -1.0202041 3.637051 -4.892852
## X167 -0.5303062 4.908629 -4.342806
## X168 -0.7038519 3.342694 -4.906275
## X169 -0.8000000 3.282892 -6.189915
## X170 -0.9416995 3.752748 -5.035953
## X171 -0.7038519 4.855724 -4.605170
## X172 -0.7350889 3.752748 -3.649659
## X174 -0.8000000 4.093428 -4.976234
## X175 -0.8864471 4.149327 -3.688879
## X176 -0.8864471 4.093428 -3.442019
## X177 -0.3147700 3.578777 -4.199705
## X178 -1.1753789 3.637051 -6.189915
## X179 -0.1026334 5.273838 -2.975930
## X180 -0.7193752 5.580204 -3.816713
## X181 -0.7510004 4.037285 -5.318520
## X182 -0.6435340 3.520211 -2.847312
## X183 -0.5577795 2.407182 -4.135167
## X184 -0.5303062 4.908629 -4.268698
## X185 -1.0202041 4.093428 -4.342806
## X186 -1.0000000 4.149327 -5.035953
## X189 -1.0619168 3.520211 -5.221356
## X190 -0.6583592 4.479850 -3.772261
## X191 -0.8510875 4.037285 -4.199705
## X192 -0.8000000 4.479850 -5.083206
## X193 -0.6288691 3.637051 -2.322788
## X194 -0.5303062 4.037285 -3.649659
## X195 -0.7834475 3.810182 -4.199705
## X197 -0.6583592 3.980894 -4.803621
## X198 -0.6583592 4.037285 -6.189915
## X200 -0.6435340 2.854653 -5.259097
## X201 -0.7510004 4.908629 -4.199705
## X202 -0.8510875 3.342694 -4.074542
## X205 -0.9607695 4.315608 -4.990833
## X208 -0.6288691 4.037285 -3.963316
## X210 -1.0000000 4.534163 -4.074542
## X212 -1.1753789 1.936058 -4.422849
## X213 -0.8000000 4.534163 -3.912023
## X214 -0.8686292 3.342694 -2.764621
## X215 -0.7350889 3.752748 -4.645992
## X216 -1.0619168 4.479850 -4.422849
## X218 -0.4125492 4.908629 -3.352407
## X219 -1.1753789 3.637051 -5.298317
## X220 -0.8864471 3.637051 -4.892852
## X223 -0.3875485 4.149327 -4.342806
## X224 -0.5577795 2.854653 -4.509860
## X225 -0.7350889 3.752748 -4.803621
## X226 -0.6583592 4.037285 -4.268698
## X227 -0.3875485 3.752748 -3.863233
## X228 -0.5303062 1.796259 -3.649659
## X229 -1.0000000 3.520211 -5.449140
## X230 -0.8000000 4.908629 -4.074542
## X231 -0.8864471 3.752748 -3.963316
## X232 -0.8510875 4.479850 -4.976234
## X233 -0.8510875 3.040333 -4.656463
## X234 -0.7193752 3.752748 -4.509860
## X236 -0.8864471 3.282892 -4.509860
## X237 -0.8000000 3.637051 -4.017384
## X239 -0.3875485 3.342694 -5.259097
## X240 -0.4900331 2.854653 -4.268698
## X241 -0.5857864 4.149327 -4.422849
## X242 -0.2000000 2.791992 -2.688248
## X243 -0.8338096 3.637051 -5.083206
## X244 -1.0202041 4.093428 -3.540459
## X245 -0.9045549 4.479850 -4.892852
## X246 -0.3386752 3.752748 -4.268698
## X247 -0.8510875 4.479850 -4.733004
## X249 -0.8000000 4.149327 -4.803621
## X250 -1.0834849 3.040333 -4.828314
## X251 -0.7510004 3.101492 -6.189915
## X253 -0.6288691 4.093428 -4.199705
## X254 -0.4900331 4.149327 -4.304754
## X255 -1.0834849 3.637051 -3.540459
## X256 -0.8864471 3.101492 -5.298317
## X257 -0.6733501 3.637051 -6.189915
## X258 -1.2254033 5.580204 -3.772261
## X260 -0.5857864 4.037285 -4.605170
## X261 -1.0202041 3.101492 -4.605170
## X262 -0.7510004 4.037285 -3.772261
## X263 -0.4379501 4.037285 -4.733004
## X264 -0.5577795 4.093428 -5.083206
## X265 -0.6583592 3.810182 -4.199705
## X267 -0.7193752 4.149327 -6.189915
## X268 -0.8510875 4.479850 -5.626821
## X269 -1.0619168 3.520211 -4.017384
## X270 -0.5857864 3.578777 -3.540459
## X271 -0.6583592 4.908629 -4.892852
## X272 -0.5577795 4.908629 -4.892852
## X273 -0.6583592 4.479850 -4.892852
## X274 -0.7510004 3.578777 -4.892852
## X275 -0.3386752 3.342694 -5.132803
## X277 -1.0000000 4.534163 -3.688879
## X278 -0.4900331 3.101492 -4.135167
## X279 -0.7671172 3.980894 -3.729701
## X281 -0.8864471 4.908629 -4.268698
## X282 -0.7834475 4.315608 -4.699481
## X283 -0.9801961 4.479850 -4.268698
## X287 -0.7834475 2.791992 -3.442019
## X289 -1.0619168 2.407182 -4.803621
## X290 -0.9607695 3.342694 -4.074542
## X291 -0.8864471 5.580204 -4.733004
## X292 -0.4900331 4.149327 -3.816713
## X294 -0.4379501 2.407182 -5.005648
## X297 -0.8864471 2.601557 -5.426151
## X298 -0.7834475 2.208489 -4.605170
## X299 -1.0000000 4.534163 -5.035953
## X301 -1.0202041 3.101492 -4.733004
## X302 -0.4900331 3.342694 -4.509860
## X303 -0.6288691 4.908629 -5.083206
## X304 -1.0202041 3.867347 -5.083206
## X305 -0.3147700 4.908629 -4.074542
## X306 -0.5577795 4.534163 -3.863233
## X307 -0.3147700 4.315608 -3.729701
## X308 -0.2450071 1.796259 -3.816713
## X311 -0.5303062 4.479850 -5.083206
## X312 -1.0202041 3.637051 -4.199705
## X313 -0.6583592 3.040333 -4.422849
## X314 -0.8510875 4.037285 -4.605170
## X315 -0.4900331 2.854653 -4.645992
## X316 -0.8686292 3.810182 -5.167289
## X317 -1.0834849 3.810182 -4.828314
## X320 -0.6288691 3.342694 -4.422849
## X321 -0.7834475 2.791992 -4.605170
## X322 -0.4508067 4.093428 -3.863233
## X323 -0.8510875 4.037285 -3.540459
## X324 -0.7350889 3.342694 -4.803621
## X325 -0.4379501 3.578777 -4.074542
## X326 -1.2000000 4.037285 -3.729701
## X327 -0.7193752 5.273838 -4.199705
## X329 -0.8864471 3.637051 -6.189915
## X330 -0.5717143 4.534163 -3.688879
## X331 -0.7510004 4.260413 -4.733004
## X332 -0.5857864 4.479850 -4.509860
## X333 -0.6583592 4.037285 -4.733004
## Thyroxine_Binding_Globulin Tissue_Factor Transferrin Trefoil_Factor_3_TFF3
## X1 -1.4271164 2.04122033 3.332205 -3.381395
## X2 -1.6094379 2.02814825 2.890372 -3.912023
## X3 -1.8971200 1.43508453 2.890372 -3.729701
## X5 -0.4780358 1.98787435 3.496508 -3.442019
## X6 -1.2378744 -0.01005034 2.995732 -4.342806
## X7 -2.1202635 1.64865863 2.708050 -3.649659
## X8 -1.3470736 0.40546511 2.833213 -4.268698
## X9 -1.4696760 0.64185389 2.564949 -4.199705
## X11 -1.3470736 1.41098697 2.772589 -4.017384
## X12 -1.2729657 1.02961942 2.995732 -4.135167
## X14 -1.2378744 1.09861229 2.995732 -3.963316
## X16 -1.1711830 1.66770682 3.091042 -2.956512
## X17 -0.9675840 0.69314718 2.708050 -4.135167
## X18 -1.9661129 0.47000363 2.564949 -4.017384
## X19 -1.8325815 0.18232156 2.639057 -4.342806
## X20 -1.1394343 1.09861229 3.091042 -4.017384
## X21 -1.8325815 0.33647224 2.564949 -3.816713
## X22 -1.7719568 1.91692261 3.178054 -3.649659
## X23 -1.6094379 0.69314718 2.302585 -4.268698
## X24 -1.6607312 1.66770682 2.995732 -3.772261
## X25 -1.9661129 0.69314718 2.564949 -4.268698
## X26 -1.6607312 1.48160454 2.772589 -3.863233
## X28 -1.7147984 1.52605630 2.890372 -3.816713
## X29 -1.7719568 0.26236426 2.484907 -4.422849
## X30 -1.8971200 0.95551145 2.564949 -4.017384
## X31 -1.3862944 0.87546874 3.091042 -3.324236
## X34 -1.2729657 0.74193734 2.772589 -3.863233
## X35 -1.3093333 0.26236426 2.833213 -4.199705
## X36 -0.6733446 0.91629073 3.526361 -3.729701
## X37 -1.7147984 1.68639895 2.890372 -3.863233
## X38 -1.6607312 1.02961942 2.772589 -4.017384
## X39 -2.0402208 1.87180218 2.944439 -3.863233
## X40 -1.2729657 0.99325177 3.091042 -3.729701
## X41 -1.2378744 0.69314718 3.178054 -4.074542
## X42 -1.7147984 1.22377543 2.708050 -4.074542
## X43 -1.7147984 0.64185389 2.772589 -4.199705
## X44 -1.5141277 1.25276297 3.091042 -3.442019
## X45 -1.2039728 1.62924054 3.044522 -3.575551
## X46 -2.0402208 1.75785792 2.944439 -3.729701
## X47 -1.7147984 0.53062825 2.484907 -4.268698
## X48 -1.8325815 1.84054963 2.639057 -3.575551
## X50 -1.6094379 1.33500107 2.944439 -4.135167
## X51 -1.4696760 1.19392247 3.044522 -3.575551
## X53 -1.5141277 1.02961942 2.772589 -3.912023
## X55 -1.6607312 1.48160454 2.833213 -3.963316
## X56 -1.0498221 1.02961942 2.944439 -3.772261
## X57 -1.3862944 1.70474809 2.944439 -3.270169
## X59 -1.4271164 1.19392247 2.944439 -3.772261
## X60 -1.8325815 0.99325177 2.708050 -3.863233
## X61 -1.8325815 0.47000363 2.833213 -3.963316
## X62 -2.3538784 0.69314718 2.564949 -4.268698
## X63 -1.8325815 1.33500107 2.708050 -3.688879
## X64 -0.6161861 0.64185389 3.218876 -3.442019
## X65 -2.4769385 1.25276297 2.708050 -4.268698
## X67 -1.9661129 1.66770682 2.890372 -4.074542
## X68 -1.3862944 1.54756251 2.890372 -3.772261
## X69 -1.9661129 0.53062825 2.397895 -4.422849
## X70 -1.9661129 1.30833282 2.708050 -4.074542
## X71 -1.7147984 1.30833282 2.833213 -3.729701
## X72 -0.7339692 2.39789527 3.555348 -3.036554
## X73 -1.6094379 1.62924054 2.995732 -3.270169
## X74 -1.6607312 -0.12783337 2.708050 -4.074542
## X75 -0.7550226 0.33647224 3.135494 -3.688879
## X76 -1.5606477 1.58923521 3.044522 -3.816713
## X77 -0.9416085 1.43508453 2.995732 -3.863233
## X78 -1.2729657 1.82454929 3.367296 -3.123566
## X80 -0.4942963 0.99325177 2.833213 -3.963316
## X81 -1.3862944 1.06471074 2.833213 -4.017384
## X82 -1.5141277 1.06471074 2.890372 -3.963316
## X83 -1.0498221 1.30833282 2.890372 -3.729701
## X84 -1.3470736 0.95551145 2.708050 -4.199705
## X85 -1.2039728 1.52605630 3.218876 -3.575551
## X86 -0.9416085 1.13140211 3.135494 -3.218876
## X88 -1.2039728 1.48160454 2.708050 -3.324236
## X90 -2.3025851 -0.21072103 1.945910 -4.677741
## X93 -1.4696760 1.02961942 2.890372 -3.649659
## X94 -0.6733446 1.82454929 3.496508 -3.079114
## X95 -1.5606477 0.74193734 2.639057 -3.963316
## X96 -1.6094379 1.48160454 2.772589 -3.963316
## X97 -1.8325815 1.66770682 2.944439 -3.772261
## X98 -1.8971200 0.87546874 2.708050 -4.074542
## X99 -1.6607312 1.75785792 3.091042 -3.473768
## X100 -1.4696760 1.43508453 3.044522 -3.912023
## X103 -1.7719568 2.16332303 3.332205 -3.352407
## X104 -1.4271164 1.30833282 2.833213 -3.963316
## X105 -1.4271164 1.62924054 3.135494 -3.772261
## X107 -1.4696760 1.45861502 3.091042 -4.017384
## X108 -0.7765288 1.38629436 3.218876 -3.218876
## X109 -2.3025851 1.02961942 2.564949 -4.268698
## X110 -1.7719568 0.58778666 2.302585 -4.268698
## X111 -1.6607312 1.38629436 2.890372 -4.074542
## X112 -1.1394343 2.11625551 3.526361 -3.442019
## X113 -0.8439701 1.43508453 3.178054 -3.863233
## X114 -1.5606477 0.33647224 2.639057 -4.199705
## X115 -0.9675840 0.91629073 2.890372 -4.074542
## X117 -1.4696760 1.28093385 3.044522 -3.411248
## X118 -1.8325815 1.64865863 2.772589 -3.540459
## X121 -1.6607312 1.68639895 3.178054 -3.442019
## X123 -1.3093333 0.18232156 2.639057 -4.017384
## X124 -0.9675840 1.16315081 2.944439 -3.729701
## X126 -1.5141277 1.19392247 2.639057 -4.268698
## X128 -0.7985077 1.13140211 3.332205 -3.381395
## X129 -1.5606477 0.58778666 2.484907 -4.135167
## X130 -1.2039728 1.22377543 3.044522 -3.649659
## X131 -1.6607312 1.28093385 2.708050 -4.268698
## X132 -1.2729657 1.33500107 3.091042 -3.611918
## X133 -1.0498221 1.02961942 3.295837 -3.729701
## X134 -1.3093333 1.54756251 2.833213 -3.729701
## X135 -1.1711830 1.66770682 3.258097 -3.244194
## X136 -1.3093333 0.69314718 2.995732 -4.017384
## X137 -1.8325815 0.99325177 2.772589 -4.342806
## X139 -1.7147984 0.74193734 2.772589 -4.135167
## X140 -1.7719568 1.45861502 2.833213 -3.912023
## X141 -0.8675006 1.36097655 2.995732 -4.199705
## X143 -1.6607312 0.33647224 2.890372 -3.649659
## X144 -1.9661129 0.99325177 2.639057 -4.342806
## X145 -2.0402208 1.28093385 2.484907 -4.342806
## X146 -0.7133499 1.22377543 3.218876 -3.540459
## X147 -1.4271164 1.68639895 2.944439 -3.729701
## X148 -0.2107210 1.30833282 3.401197 -3.688879
## X149 -2.0402208 1.16315081 2.772589 -4.268698
## X152 -1.0216512 2.17475172 3.295837 -3.411248
## X153 -0.6161861 1.19392247 3.367296 -3.963316
## X154 -2.0402208 1.22377543 2.833213 -3.863233
## X155 -1.3470736 1.22377543 2.772589 -4.199705
## X156 -1.2039728 1.43508453 3.135494 -3.772261
## X157 -1.4696760 1.38629436 2.890372 -3.729701
## X158 -1.2039728 1.54756251 2.708050 -4.074542
## X159 -1.6607312 0.83290912 2.639057 -4.342806
## X160 -1.8971200 1.74046617 3.218876 -3.863233
## X161 -1.2039728 1.06471074 3.091042 -3.729701
## X162 -2.0402208 1.75785792 2.708050 -4.017384
## X163 -2.0402208 1.09861229 2.708050 -4.422849
## X165 -2.0402208 1.62924054 2.639057 -3.963316
## X166 -2.3025851 1.13140211 2.708050 -4.199705
## X167 -1.5606477 1.96009478 3.218876 -3.575551
## X168 -1.6094379 1.48160454 2.890372 -4.017384
## X169 -1.7719568 0.69314718 2.890372 -4.199705
## X170 -1.3862944 0.33647224 2.772589 -4.268698
## X171 -0.9942523 1.48160454 3.178054 -3.688879
## X172 -1.6607312 1.19392247 2.772589 -4.199705
## X174 -0.9675840 0.87546874 3.135494 -3.688879
## X175 -0.8439701 1.41098697 3.135494 -3.611918
## X176 -1.0788097 1.06471074 2.890372 -3.863233
## X177 -1.7147984 1.52605630 3.044522 -3.729701
## X178 -1.8971200 0.53062825 2.564949 -4.017384
## X179 -0.8728085 0.58778666 3.583519 -3.079114
## X180 -1.5606477 1.45861502 2.944439 -3.649659
## X181 -1.8325815 0.47000363 2.564949 -4.268698
## X182 -1.7719568 1.19392247 2.833213 -3.863233
## X183 -1.3862944 0.00000000 3.091042 -3.816713
## X184 -1.8325815 1.25276297 3.218876 -3.688879
## X185 -1.0788097 1.06471074 3.178054 -3.863233
## X186 -1.7147984 0.95551145 2.833213 -4.017384
## X189 -1.9661129 1.30833282 2.708050 -4.744432
## X190 -1.5141277 1.62924054 2.772589 -3.912023
## X191 -1.5606477 1.09861229 2.833213 -4.135167
## X192 -1.5606477 2.10413415 3.091042 -3.688879
## X193 -1.3862944 1.54756251 3.044522 -3.772261
## X194 -1.5141277 0.83290912 2.639057 -4.135167
## X195 -1.7719568 2.11625551 3.258097 -3.688879
## X197 -1.2378744 1.54756251 3.044522 -3.575551
## X198 -1.5141277 0.78845736 2.708050 -4.342806
## X200 -1.8971200 1.38629436 3.178054 -3.912023
## X201 -1.3470736 0.74193734 2.708050 -3.863233
## X202 -2.1202635 0.58778666 2.564949 -4.199705
## X205 -1.7147984 1.36097655 2.772589 -3.772261
## X208 -0.7133499 1.25276297 3.135494 -3.912023
## X210 -1.2729657 1.36097655 3.135494 -3.506558
## X212 -1.4696760 0.91629073 2.772589 -3.863233
## X213 -1.0788097 1.41098697 3.044522 -3.772261
## X214 -1.7147984 1.38629436 2.833213 -3.540459
## X215 -1.9661129 1.38629436 2.772589 -4.135167
## X216 -1.1394343 0.74193734 2.890372 -4.268698
## X218 -0.8675006 0.83290912 3.295837 -3.729701
## X219 -2.0402208 0.83290912 2.639057 -3.963316
## X220 -1.7147984 1.16315081 2.772589 -4.135167
## X223 -1.3470736 1.41098697 3.218876 -3.863233
## X224 -1.8325815 1.48160454 2.944439 -4.199705
## X225 -1.5606477 2.39789527 2.995732 -3.473768
## X226 -1.5606477 1.85629799 3.044522 -3.352407
## X227 -0.9675840 1.16315081 3.496508 -3.912023
## X228 -1.8971200 1.06471074 2.708050 -4.509860
## X229 -1.6607312 0.69314718 2.772589 -4.199705
## X230 -1.8325815 0.95551145 2.890372 -3.649659
## X231 -1.4696760 0.83290912 2.639057 -4.268698
## X232 -1.3470736 0.91629073 2.833213 -4.135167
## X233 -1.4696760 0.47000363 2.639057 -4.422849
## X234 -1.1394343 0.95551145 2.833213 -4.017384
## X236 -1.8971200 1.13140211 2.484907 -4.342806
## X237 -0.9675840 1.19392247 2.890372 -3.506558
## X239 -1.9661129 0.18232156 2.302585 -4.268698
## X240 -1.7147984 0.47000363 2.639057 -4.074542
## X241 -0.9942523 1.43508453 3.044522 -3.540459
## X242 -0.9535408 0.47000363 3.285794 -3.729701
## X243 -1.2378744 0.58778666 2.772589 -4.135167
## X244 -1.4271164 1.36097655 2.833213 -3.611918
## X245 -2.0402208 0.09531018 1.931521 -4.744432
## X246 -1.2039728 0.78845736 3.178054 -3.411248
## X247 -2.1202635 0.74193734 2.397895 -4.199705
## X249 -2.1202635 2.11625551 2.833213 -3.649659
## X250 -1.5141277 1.38629436 2.944439 -3.772261
## X251 -1.7147984 0.83290912 2.708050 -4.135167
## X253 -0.8439701 1.06471074 2.890372 -3.729701
## X254 -1.0788097 2.48490665 3.367296 -3.123566
## X255 -1.1086626 1.70474809 3.218876 -3.381395
## X256 -2.2072749 1.19392247 2.564949 -4.199705
## X257 -1.9661129 0.64185389 2.302585 -4.509860
## X258 -1.2039728 1.41098697 3.401197 -3.442019
## X260 -1.2729657 1.16315081 2.772589 -4.135167
## X261 -1.7147984 0.58778666 2.564949 -4.199705
## X262 -1.8325815 0.78845736 2.484907 -4.605170
## X263 -0.9675840 1.48160454 3.091042 -3.194183
## X264 -0.6539265 1.28093385 2.944439 -3.772261
## X265 -1.3470736 1.30833282 2.890372 -3.816713
## X267 -1.7147984 1.43508453 3.091042 -3.473768
## X268 -1.7719568 1.50407740 2.772589 -3.688879
## X269 -1.7719568 0.74193734 2.772589 -4.135167
## X270 -1.8971200 1.85629799 2.995732 -3.729701
## X271 -1.2378744 1.41098697 2.995732 -3.506558
## X272 -1.1394343 1.36097655 2.995732 -3.772261
## X273 -1.5141277 1.28093385 2.772589 -3.540459
## X274 -1.5141277 1.30833282 2.833213 -4.342806
## X275 -1.3093333 1.98787435 3.044522 -3.381395
## X277 -1.0788097 0.87546874 3.091042 -4.509860
## X278 -1.3862944 1.25276297 3.044522 -3.442019
## X279 -1.4271164 0.58778666 2.890372 -3.772261
## X281 -1.5606477 1.38629436 2.944439 -3.863233
## X282 -1.2378744 0.83290912 2.890372 -4.199705
## X283 -1.2378744 1.56861592 3.135494 -3.244194
## X287 -1.7147984 1.70474809 3.044522 -3.912023
## X289 -1.7147984 0.64185389 2.708050 -4.422849
## X290 -1.7719568 0.95551145 2.708050 -4.017384
## X291 -1.5606477 1.72276660 2.890372 -4.017384
## X292 -1.0216512 1.87180218 3.135494 -3.411248
## X294 -1.8971200 0.47000363 2.564949 -4.135167
## X297 -1.6607312 1.13140211 2.772589 -3.963316
## X298 -1.7719568 0.53062825 3.044522 -3.963316
## X299 -1.8325815 1.19392247 2.890372 -3.816713
## X301 -1.8971200 0.40546511 2.639057 -4.017384
## X302 -1.4696760 0.09531018 2.772589 -4.135167
## X303 -1.3470736 1.16315081 2.995732 -3.816713
## X304 -1.6607312 2.02814825 2.944439 -3.912023
## X305 -1.4271164 1.22377543 2.890372 -3.575551
## X306 -0.8915981 1.84054963 3.761200 -3.218876
## X307 -0.8209806 1.52605630 3.367296 -3.352407
## X308 -1.2729657 1.58923521 3.258097 -3.473768
## X311 -1.2039728 0.87546874 2.833213 -3.912023
## X312 -1.2378744 1.56861592 2.995732 -3.729701
## X313 -1.2729657 0.99325177 2.944439 -4.017384
## X314 -1.7147984 1.43508453 2.708050 -4.074542
## X315 -0.9942523 0.99325177 3.295837 -3.540459
## X316 -1.7719568 1.64865863 2.890372 -4.268698
## X317 -1.6607312 1.70474809 2.890372 -3.772261
## X320 -1.3093333 0.74193734 3.332205 -3.816713
## X321 -1.2378744 -0.06187540 3.044522 -4.017384
## X322 -1.2729657 1.56861592 3.218876 -3.575551
## X323 -1.3862944 1.45861502 2.944439 -3.649659
## X324 -2.0402208 0.91629073 2.282382 -3.863233
## X325 -0.9675840 1.33500107 3.044522 -3.575551
## X326 -1.6607312 0.91629073 2.772589 -3.963316
## X327 -1.5606477 1.28093385 2.995732 -4.074542
## X329 -1.8971200 1.41098697 2.944439 -3.863233
## X330 -0.4780358 0.47000363 3.496508 -4.268698
## X331 -1.6607312 0.74193734 2.639057 -4.605170
## X332 -1.2729657 0.18232156 2.890372 -4.074542
## X333 -1.4696760 2.17475172 3.367296 -3.863233
## VCAM_1 VEGF Vitronectin von_Willebrand_Factor E4 E3 E2
## X1 3.258097 22.03456 -0.04082199 -3.146555 1 2 1
## X2 2.708050 18.60184 -0.38566248 -3.863233 2 2 1
## X3 2.639057 17.47619 -0.22314355 -3.540459 2 2 1
## X5 3.044522 20.77860 0.16621555 -3.816713 1 2 1
## X6 2.208274 13.19761 0.26236426 -4.509860 2 1 1
## X7 2.639057 17.91139 -0.37106368 -4.017384 1 2 2
## X8 2.564949 13.26878 0.00000000 -4.199705 1 2 2
## X9 2.564949 15.77258 -0.82098055 -4.268698 1 2 1
## X11 2.708050 15.65264 -0.11653382 -3.611918 2 2 1
## X12 2.397895 17.16420 -0.15082289 -4.199705 1 2 2
## X14 2.833213 15.95757 -0.09431068 -3.442019 2 2 1
## X16 3.218876 17.47619 -0.19845094 -3.863233 2 2 1
## X17 3.135494 13.14977 -0.30110509 -4.199705 1 2 1
## X18 2.564949 14.00853 -0.57981850 -3.816713 2 2 1
## X19 2.890372 15.09899 -0.49429632 -4.268698 1 2 1
## X20 2.639057 17.29317 -0.11653382 -3.506558 2 1 2
## X21 2.639057 13.72601 -0.65392647 -4.268698 2 2 1
## X22 2.944439 19.75007 -0.63487827 -3.611918 1 2 1
## X23 1.945910 14.83572 -0.44628710 -4.342806 2 2 1
## X24 2.890372 17.17862 -0.63487827 -3.649659 1 2 1
## X25 2.302585 15.38951 -0.49429632 -4.342806 2 1 1
## X26 2.397895 16.85569 -0.73396918 -3.863233 2 2 1
## X28 2.833213 16.42640 -0.12783337 -4.342806 1 2 1
## X29 2.230014 14.70067 -0.51082562 -4.342806 2 1 1
## X30 2.397895 15.08048 -0.52763274 -4.342806 1 2 2
## X31 2.995732 17.09173 -0.17435339 -3.863233 1 2 1
## X34 2.772589 15.53091 -0.09431068 -4.074542 2 2 1
## X35 2.028148 14.98724 -0.23572233 -4.342806 2 2 1
## X36 3.135494 15.72139 0.33647224 -3.611918 1 2 2
## X37 2.564949 17.14975 -0.75502258 -3.729701 2 2 1
## X38 2.639057 15.66988 -0.61618614 -3.963316 1 2 1
## X39 2.833213 18.65101 -0.49429632 -3.575551 1 2 1
## X40 2.639057 16.10590 -0.21072103 -3.575551 1 2 1
## X41 2.708050 14.91184 0.09531018 -4.017384 1 2 1
## X42 2.708050 17.34988 -0.43078292 -4.074542 2 2 1
## X43 2.302585 14.96846 -0.40047757 -4.135167 1 2 1
## X44 2.995732 18.23746 -0.22314355 -3.575551 2 2 1
## X45 3.258097 17.09173 -0.26136476 -3.912023 1 2 1
## X46 2.708050 18.91693 -0.65392647 -3.411248 1 2 1
## X47 2.397895 15.35377 -0.49429632 -4.342806 1 2 1
## X48 3.091042 19.83721 -0.69314718 -3.170086 1 2 2
## X50 2.890372 17.14975 -0.19845094 -3.688879 1 2 1
## X51 2.944439 19.04716 -0.01005034 -3.381395 1 2 1
## X53 2.772589 17.76415 -0.30110509 -4.074542 1 2 1
## X55 2.564949 18.18606 -0.43078292 -3.912023 2 1 1
## X56 2.708050 16.78059 -0.01005034 -3.863233 1 2 1
## X57 2.944439 19.62899 -0.65392647 -3.506558 1 2 1
## X59 3.091042 16.05675 -0.44628710 -3.218876 2 2 1
## X60 2.708050 15.77258 -0.37106368 -4.135167 1 2 1
## X61 2.708050 15.53091 -0.40047757 -3.912023 1 2 1
## X62 2.028148 16.61303 -0.61618614 -4.342806 1 2 1
## X63 2.833213 16.70483 -0.31471074 -3.649659 1 2 1
## X64 2.708050 15.75555 0.09531018 -3.963316 2 2 1
## X65 2.564949 16.42640 -0.96758403 -3.863233 1 2 1
## X67 2.484907 17.44828 -0.96758403 -3.963316 2 2 1
## X68 2.639057 18.14732 -0.26136476 -4.342806 1 2 1
## X69 2.282382 14.70067 -0.43078292 -3.963316 1 2 1
## X70 2.890372 18.25027 -0.63487827 -3.816713 1 2 1
## X71 2.484907 17.81797 -0.37106368 -3.649659 1 2 2
## X72 3.610918 19.86969 0.47000363 -3.490408 1 2 1
## X73 3.091042 19.29097 -0.56211892 -3.411248 1 2 2
## X74 2.302585 13.98717 -0.05129329 -4.605170 1 2 1
## X75 2.772589 15.02467 -0.01005034 -3.575551 1 2 1
## X76 2.772589 18.14732 -0.40047757 -3.863233 2 2 1
## X77 2.639057 17.53176 -0.01005034 -3.442019 2 2 1
## X78 3.044522 20.00922 -0.09431068 -3.352407 2 2 1
## X80 3.091042 19.12912 0.09531018 -3.912023 1 2 1
## X81 2.639057 15.77258 -0.23572233 -4.017384 1 2 1
## X82 2.772589 16.28369 -0.51082562 -4.017384 2 2 1
## X83 2.772589 17.50402 0.00000000 -4.721704 1 2 2
## X84 2.484907 16.95974 -0.47803580 -3.963316 2 2 1
## X85 2.890372 20.15734 -0.09431068 -3.442019 1 2 1
## X86 3.295837 16.73521 -0.04082199 -3.218876 1 2 2
## X88 3.178054 17.84476 -0.31471074 -3.729701 2 2 1
## X90 2.292535 14.07222 -1.13943428 -4.990833 1 2 1
## X93 2.833213 18.09542 -0.51082562 -3.442019 2 2 1
## X94 3.688879 20.79835 0.18232156 -3.218876 1 2 1
## X95 2.890372 15.80652 -0.57981850 -3.688879 1 2 1
## X96 2.639057 17.61447 -0.32850407 -3.863233 2 2 1
## X97 2.564949 17.23608 -0.89159812 -4.199705 2 2 1
## X98 2.197225 16.02382 -0.46203546 -4.509860 1 2 2
## X99 2.944439 17.62818 -0.41551544 -3.079114 1 2 1
## X100 2.397895 18.36473 0.09531018 -3.772261 2 2 1
## X103 3.044522 20.92578 -0.65392647 -3.324236 1 2 2
## X104 2.772589 19.23348 -0.40047757 -3.688879 2 2 1
## X105 2.833213 18.41515 -0.23572233 -3.912023 1 2 1
## X107 2.708050 18.12141 -0.07257069 -3.912023 2 2 1
## X108 2.833213 18.67549 0.18232156 -4.074542 1 2 1
## X109 2.128232 15.75555 -0.77652879 -4.342806 1 2 2
## X110 1.960095 13.38584 -0.99425227 -3.912023 1 2 2
## X111 2.772589 19.35952 -0.59783700 -4.017384 2 2 1
## X112 3.044522 18.88110 0.26236426 -3.575551 1 2 1
## X113 2.944439 15.68709 0.26236426 -3.506558 2 2 1
## X114 2.079442 13.36258 -0.15082289 -4.268698 2 2 1
## X115 2.833213 15.13589 -0.02020271 -4.017384 2 2 1
## X117 2.995732 17.92466 -0.22314355 -3.816713 1 2 1
## X118 2.995732 17.66918 -0.57981850 -3.575551 1 2 1
## X121 3.044522 16.82573 -0.41551544 -3.194183 2 2 1
## X123 2.251292 14.19800 -0.54472718 -4.017384 2 2 1
## X124 2.708050 16.94495 0.33647224 -3.729701 1 2 1
## X126 2.564949 15.99076 -0.57981850 -4.074542 2 2 1
## X128 2.708050 18.65101 0.18232156 -3.816713 2 2 1
## X129 2.397895 13.92275 -0.61618614 -4.605170 2 2 1
## X130 2.772589 17.46224 -0.16251893 -3.863233 1 2 1
## X131 2.282382 17.01865 -0.49429632 -4.422849 1 2 1
## X132 2.639057 18.17317 -0.22314355 -3.963316 2 1 1
## X133 2.944439 16.26768 -0.08338161 -3.963316 2 2 1
## X134 2.944439 17.40624 -0.27443685 -3.816713 2 2 1
## X135 2.833213 18.51518 -0.18632958 -3.611918 1 2 2
## X136 2.708050 17.68281 -0.23572233 -3.649659 2 2 1
## X137 2.128232 16.61303 -0.51082562 -3.649659 2 1 2
## X139 2.251292 15.82344 -0.19845094 -4.268698 2 2 1
## X140 2.639057 16.75036 -0.24846136 -3.963316 2 1 2
## X141 2.564949 17.96435 0.09531018 -4.017384 2 2 1
## X143 2.772589 16.65905 -0.03045921 -4.074542 1 2 1
## X144 2.708050 14.19800 -0.27443685 -3.863233 2 2 1
## X145 2.302585 16.07317 -0.77652879 -4.342806 1 2 1
## X146 3.044522 17.62818 -0.09431068 -3.442019 2 2 1
## X147 2.708050 17.37811 -0.52763274 -3.381395 2 2 1
## X148 2.833213 18.63874 0.47000363 -4.017384 1 2 1
## X149 2.302585 16.68960 -0.35667494 -3.963316 1 2 2
## X152 3.332205 17.76415 -0.04082199 -3.218876 2 2 1
## X153 2.890372 16.81071 0.18232156 -3.912023 1 2 1
## X154 2.397895 16.13851 -0.41551544 -4.199705 1 2 1
## X155 2.302585 15.40733 -0.15082289 -4.135167 2 1 1
## X156 2.833213 18.62646 -0.09431068 -3.540459 1 2 1
## X157 3.135494 17.07716 -0.40047757 -3.324236 1 2 2
## X158 2.639057 17.89810 -0.59783700 -3.772261 2 2 1
## X159 2.772589 14.70067 -0.38566248 -3.611918 1 2 1
## X160 2.944439 19.49518 -0.35667494 -3.729701 1 2 2
## X161 2.833213 17.61447 -0.02020271 -3.963316 1 2 1
## X162 2.564949 18.42771 -0.73396918 -3.963316 1 2 1
## X163 2.066863 16.10590 -0.57981850 -4.615221 2 2 1
## X165 2.890372 16.47344 -0.94160854 -3.575551 2 2 1
## X166 2.208274 16.18718 -0.71334989 -4.135167 1 2 1
## X167 2.995732 19.88049 -0.71334989 -4.422849 1 2 1
## X168 2.772589 16.95974 -0.08338161 -4.199705 1 2 1
## X169 2.251292 15.51337 -0.54472718 -4.199705 1 2 2
## X170 2.397895 13.85775 -0.02020271 -3.963316 1 2 1
## X171 3.091042 16.41066 0.09531018 -4.074542 1 2 1
## X172 2.564949 17.60073 -0.30110509 -4.509860 1 2 1
## X174 2.772589 16.73521 0.26236426 -3.912023 1 2 1
## X175 2.772589 16.56675 0.09531018 -4.268698 1 2 1
## X176 2.708050 16.25164 -0.01005034 -3.575551 1 2 1
## X177 2.708050 18.49027 -0.47803580 -3.772261 1 2 1
## X178 1.931521 15.02467 -1.42711636 -4.422849 1 2 1
## X179 2.772589 18.27583 0.53062825 -3.772261 1 2 2
## X180 3.295837 15.87400 -0.51082562 -3.912023 1 2 1
## X181 2.397895 16.28369 -0.34249031 -4.268698 1 2 1
## X182 2.564949 18.41515 -0.49429632 -3.649659 1 2 2
## X183 2.484907 14.54339 -0.04082199 -3.863233 2 2 1
## X184 2.708050 18.33941 -0.69314718 -3.611918 2 2 1
## X185 2.302585 18.06935 -0.01005034 -3.649659 2 2 1
## X186 2.261763 15.42510 -0.47803580 -4.074542 2 2 1
## X189 2.484907 17.47619 -0.84397007 -3.772261 1 2 1
## X190 2.772589 19.25652 -0.59783700 -3.963316 1 2 2
## X191 2.708050 16.88555 -0.10536052 -3.963316 2 1 1
## X192 3.044522 19.21038 -0.47803580 -4.074542 2 1 2
## X193 3.178054 15.78957 -0.37106368 -3.381395 2 1 1
## X194 2.302585 16.44211 -0.41551544 -4.074542 2 2 1
## X195 3.044522 17.64187 -0.31471074 -3.540459 1 2 1
## X197 3.044522 17.95114 0.00000000 -3.611918 2 2 1
## X198 2.484907 15.75555 -0.46203546 -4.755993 2 1 2
## X200 2.772589 19.40495 -0.51082562 -3.575551 2 2 1
## X201 2.484907 18.08239 -0.22314355 -4.268698 1 2 1
## X202 2.302585 14.23946 -0.69314718 -4.422849 2 2 1
## X205 2.772589 16.93014 -0.54472718 -3.863233 1 2 1
## X208 2.772589 17.96435 0.26236426 -3.649659 2 2 1
## X210 2.772589 16.88555 -0.41551544 -3.473768 2 2 1
## X212 2.772589 15.17262 -0.21072103 -4.342806 2 1 1
## X213 2.708050 16.72003 -0.05129329 -3.611918 2 2 1
## X214 2.995732 16.08955 -0.18632958 -2.956512 1 2 1
## X215 2.302585 17.06257 -0.89159812 -3.963316 1 2 1
## X216 2.708050 17.60073 0.09531018 -4.342806 2 2 1
## X218 2.833213 16.28369 0.33647224 -4.074542 1 2 1
## X219 2.397895 16.55127 -0.41551544 -4.268698 1 2 1
## X220 2.302585 15.40733 -0.44628710 -4.135167 1 2 1
## X223 2.639057 17.14975 0.00000000 -4.135167 2 2 1
## X224 2.639057 17.83137 -0.37106368 -3.729701 1 2 1
## X225 3.135494 20.29296 -0.69314718 -3.473768 1 2 1
## X226 3.091042 21.33654 -0.41551544 -3.442019 1 2 1
## X227 2.708050 18.37736 0.24233051 -3.772261 1 2 1
## X228 2.272126 14.75884 -0.63487827 -4.268698 1 2 1
## X229 2.230014 12.70378 -0.15082289 -4.755993 2 1 1
## X230 2.833213 18.03012 -0.26136476 -3.411248 1 2 2
## X231 2.484907 17.17862 -0.26136476 -3.912023 1 2 1
## X232 2.397895 18.19894 -0.24846136 -4.135167 1 2 2
## X233 2.041220 15.19092 -0.22314355 -4.656463 2 1 1
## X234 2.708050 15.75555 -0.12783337 -4.268698 1 2 2
## X236 2.208274 15.63536 -0.44628710 -4.268698 1 2 1
## X237 2.564949 16.78059 0.00000000 -4.199705 1 2 1
## X239 2.302585 11.83075 -0.82098055 -4.268698 1 2 1
## X240 2.564949 15.58331 -0.47803580 -3.912023 1 2 1
## X241 2.397895 17.88480 0.26236426 -3.912023 1 2 1
## X242 2.995732 14.34213 0.14249286 -3.729701 1 2 1
## X243 2.484907 14.05105 0.18232156 -3.963316 1 2 1
## X244 2.944439 17.43429 -0.43078292 -3.506558 2 2 1
## X245 1.722767 13.50102 -0.77652879 -4.422849 1 2 1
## X246 2.890372 17.23608 0.18232156 -3.473768 1 2 2
## X247 2.151762 16.26768 -0.73396918 -4.342806 2 2 1
## X249 3.044522 19.12912 -0.65392647 -3.270169 2 1 2
## X250 2.639057 17.88480 -0.02020271 -3.772261 2 1 2
## X251 2.564949 17.32157 -0.67334455 -4.017384 2 2 1
## X253 2.944439 17.50402 -0.03045921 -3.411248 1 2 1
## X254 3.637586 22.38015 0.00000000 -3.218876 1 2 1
## X255 2.890372 17.26467 0.00000000 -3.473768 2 2 1
## X256 2.282382 14.83572 -0.84397007 -4.268698 2 2 1
## X257 2.272126 14.89288 -0.63487827 -4.199705 2 2 1
## X258 2.890372 18.73643 0.00000000 -3.296837 2 2 1
## X260 2.639057 17.26467 0.09531018 -4.017384 1 2 1
## X261 2.128232 14.13537 -0.59783700 -4.199705 1 2 1
## X262 2.079442 16.90044 -0.44628710 -4.422849 1 2 1
## X263 2.890372 19.52881 0.26236426 -3.270169 1 2 1
## X264 3.044522 17.51790 0.09531018 -4.074542 2 2 1
## X265 2.639057 17.89810 -0.22314355 -4.422849 1 2 1
## X267 2.944439 18.18606 -0.56211892 -3.352407 2 2 1
## X268 2.564949 18.60184 -0.21072103 -3.963316 1 2 1
## X269 2.397895 16.85569 -0.47803580 -4.509860 2 2 1
## X270 3.178054 18.19894 -0.65392647 -3.729701 1 2 1
## X271 2.944439 18.09542 -0.09431068 -3.442019 2 2 1
## X272 2.484907 18.73643 0.09531018 -4.509860 1 2 2
## X273 3.044522 17.51790 -0.34249031 -4.074542 1 2 1
## X274 2.564949 17.20740 -0.23572233 -3.912023 1 2 1
## X275 2.833213 18.21180 -0.13926207 -3.611918 1 2 2
## X277 2.833213 16.10590 0.18232156 -3.611918 1 2 1
## X278 3.044522 17.61447 -0.08338161 -3.863233 1 2 1
## X279 2.639057 16.39490 -0.21072103 -3.963316 2 2 1
## X281 2.833213 15.90753 -0.47803580 -3.912023 2 2 1
## X282 2.484907 17.12079 0.09531018 -3.540459 2 2 1
## X283 3.091042 21.12839 -0.32850407 -3.649659 1 2 2
## X287 2.302585 17.87147 -0.22314355 -3.540459 1 2 2
## X289 2.397895 14.91184 -0.21072103 -4.342806 1 2 1
## X290 2.564949 14.77813 -0.57981850 -3.729701 1 2 1
## X291 2.292535 15.22740 -0.31471074 -3.729701 1 2 2
## X292 3.218876 19.42759 0.09531018 -4.199705 1 2 1
## X294 2.564949 13.92275 -0.47803580 -4.342806 1 1 2
## X297 2.564949 16.62839 -0.56211892 -4.017384 1 2 1
## X298 2.772589 16.37910 -0.12783337 -3.772261 1 2 1
## X299 2.708050 16.87063 -0.69314718 -3.863233 2 2 1
## X301 2.890372 15.19092 0.00000000 -4.268698 2 2 1
## X302 2.066863 14.40307 -0.19845094 -4.947660 1 2 1
## X303 2.772589 17.76415 0.18232156 -4.074542 1 2 1
## X304 2.708050 17.80454 -0.75502258 -3.688879 2 1 1
## X305 3.135494 21.09010 -0.43078292 -4.017384 1 2 1
## X306 2.995732 19.25652 0.26236426 -3.352407 1 2 2
## X307 2.772589 18.45279 0.18232156 -3.575551 1 2 1
## X308 2.944439 17.92466 -0.10536052 -3.816713 2 2 1
## X311 2.484907 15.42510 -0.18632958 -3.912023 2 2 1
## X312 2.833213 20.78848 -0.05129329 -3.772261 2 1 2
## X313 2.708050 16.41066 0.09531018 -4.268698 1 2 1
## X314 2.708050 16.88555 -0.71334989 -4.268698 1 2 1
## X315 2.833213 16.58220 0.18232156 -3.729701 1 2 1
## X316 2.564949 17.49011 -0.61618614 -4.017384 1 2 1
## X317 2.639057 17.61447 -0.40047757 -3.296837 1 2 1
## X320 2.944439 16.41066 0.47000363 -4.605170 2 2 1
## X321 2.397895 12.70378 0.26236426 -4.635629 1 2 1
## X322 3.178054 17.73712 -0.04082199 -3.575551 1 1 2
## X323 3.135494 18.54002 -0.61618614 -3.352407 1 2 1
## X324 2.484907 15.87400 -0.69314718 -3.473768 2 2 1
## X325 3.044522 18.42771 0.18232156 -3.575551 1 2 2
## X326 2.890372 16.97451 -0.69314718 -4.268698 2 2 1
## X327 2.564949 19.05891 -0.44628710 -3.649659 2 2 1
## X329 2.292535 17.51790 -0.44628710 -3.729701 1 2 1
## X330 2.564949 15.61805 0.20543916 -4.509860 2 2 1
## X331 2.302585 14.54339 -0.47803580 -4.509860 1 2 1
## X332 2.564949 16.36327 0.18232156 -4.342806 1 2 1
## X333 2.944439 22.34608 -0.32850407 -4.074542 2 2 1
##
## $usekernel
## [1] TRUE
##
## $varnames
## [1] "ACE_CD143_Angiotensin_Converti" "ACTH_Adrenocorticotropic_Hormon"
## [3] "AXL" "Adiponectin"
## [5] "Alpha_1_Antichymotrypsin" "Alpha_1_Antitrypsin"
## [7] "Alpha_1_Microglobulin" "Alpha_2_Macroglobulin"
## [9] "Angiopoietin_2_ANG_2" "Angiotensinogen"
## [11] "Apolipoprotein_A_IV" "Apolipoprotein_A1"
## [13] "Apolipoprotein_A2" "Apolipoprotein_B"
## [15] "Apolipoprotein_CI" "Apolipoprotein_CIII"
## [17] "Apolipoprotein_D" "Apolipoprotein_E"
## [19] "Apolipoprotein_H" "B_Lymphocyte_Chemoattractant_BL"
## [21] "BMP_6" "Beta_2_Microglobulin"
## [23] "Betacellulin" "C_Reactive_Protein"
## [25] "CD40" "CD5L"
## [27] "Calbindin" "Calcitonin"
## [29] "CgA" "Clusterin_Apo_J"
## [31] "Complement_3" "Complement_Factor_H"
## [33] "Connective_Tissue_Growth_Factor" "Cortisol"
## [35] "Creatine_Kinase_MB" "Cystatin_C"
## [37] "EGF_R" "EN_RAGE"
## [39] "ENA_78" "Eotaxin_3"
## [41] "FAS" "FSH_Follicle_Stimulation_Hormon"
## [43] "Fas_Ligand" "Fatty_Acid_Binding_Protein"
## [45] "Ferritin" "Fetuin_A"
## [47] "Fibrinogen" "GRO_alpha"
## [49] "Gamma_Interferon_induced_Monokin" "Glutathione_S_Transferase_alpha"
## [51] "HB_EGF" "HCC_4"
## [53] "Hepatocyte_Growth_Factor_HGF" "I_309"
## [55] "ICAM_1" "IGF_BP_2"
## [57] "IL_11" "IL_13"
## [59] "IL_16" "IL_17E"
## [61] "IL_1alpha" "IL_3"
## [63] "IL_4" "IL_5"
## [65] "IL_6" "IL_6_Receptor"
## [67] "IL_7" "IL_8"
## [69] "IP_10_Inducible_Protein_10" "IgA"
## [71] "Insulin" "Kidney_Injury_Molecule_1_KIM_1"
## [73] "LOX_1" "Leptin"
## [75] "Lipoprotein_a" "MCP_1"
## [77] "MCP_2" "MIF"
## [79] "MIP_1alpha" "MIP_1beta"
## [81] "MMP_2" "MMP_3"
## [83] "MMP10" "MMP7"
## [85] "Myoglobin" "NT_proBNP"
## [87] "NrCAM" "Osteopontin"
## [89] "PAI_1" "PAPP_A"
## [91] "PLGF" "PYY"
## [93] "Pancreatic_polypeptide" "Prolactin"
## [95] "Prostatic_Acid_Phosphatase" "Protein_S"
## [97] "Pulmonary_and_Activation_Regulat" "RANTES"
## [99] "Resistin" "S100b"
## [101] "SGOT" "SHBG"
## [103] "SOD" "Serum_Amyloid_P"
## [105] "Sortilin" "Stem_Cell_Factor"
## [107] "TGF_alpha" "TIMP_1"
## [109] "TNF_RII" "TRAIL_R3"
## [111] "TTR_prealbumin" "Tamm_Horsfall_Protein_THP"
## [113] "Thrombomodulin" "Thrombopoietin"
## [115] "Thymus_Expressed_Chemokine_TECK" "Thyroid_Stimulating_Hormone"
## [117] "Thyroxine_Binding_Globulin" "Tissue_Factor"
## [119] "Transferrin" "Trefoil_Factor_3_TFF3"
## [121] "VCAM_1" "VEGF"
## [123] "Vitronectin" "von_Willebrand_Factor"
## [125] "E4" "E3"
## [127] "E2"
##
## $xNames
## [1] "ACE_CD143_Angiotensin_Converti" "ACTH_Adrenocorticotropic_Hormon"
## [3] "AXL" "Adiponectin"
## [5] "Alpha_1_Antichymotrypsin" "Alpha_1_Antitrypsin"
## [7] "Alpha_1_Microglobulin" "Alpha_2_Macroglobulin"
## [9] "Angiopoietin_2_ANG_2" "Angiotensinogen"
## [11] "Apolipoprotein_A_IV" "Apolipoprotein_A1"
## [13] "Apolipoprotein_A2" "Apolipoprotein_B"
## [15] "Apolipoprotein_CI" "Apolipoprotein_CIII"
## [17] "Apolipoprotein_D" "Apolipoprotein_E"
## [19] "Apolipoprotein_H" "B_Lymphocyte_Chemoattractant_BL"
## [21] "BMP_6" "Beta_2_Microglobulin"
## [23] "Betacellulin" "C_Reactive_Protein"
## [25] "CD40" "CD5L"
## [27] "Calbindin" "Calcitonin"
## [29] "CgA" "Clusterin_Apo_J"
## [31] "Complement_3" "Complement_Factor_H"
## [33] "Connective_Tissue_Growth_Factor" "Cortisol"
## [35] "Creatine_Kinase_MB" "Cystatin_C"
## [37] "EGF_R" "EN_RAGE"
## [39] "ENA_78" "Eotaxin_3"
## [41] "FAS" "FSH_Follicle_Stimulation_Hormon"
## [43] "Fas_Ligand" "Fatty_Acid_Binding_Protein"
## [45] "Ferritin" "Fetuin_A"
## [47] "Fibrinogen" "GRO_alpha"
## [49] "Gamma_Interferon_induced_Monokin" "Glutathione_S_Transferase_alpha"
## [51] "HB_EGF" "HCC_4"
## [53] "Hepatocyte_Growth_Factor_HGF" "I_309"
## [55] "ICAM_1" "IGF_BP_2"
## [57] "IL_11" "IL_13"
## [59] "IL_16" "IL_17E"
## [61] "IL_1alpha" "IL_3"
## [63] "IL_4" "IL_5"
## [65] "IL_6" "IL_6_Receptor"
## [67] "IL_7" "IL_8"
## [69] "IP_10_Inducible_Protein_10" "IgA"
## [71] "Insulin" "Kidney_Injury_Molecule_1_KIM_1"
## [73] "LOX_1" "Leptin"
## [75] "Lipoprotein_a" "MCP_1"
## [77] "MCP_2" "MIF"
## [79] "MIP_1alpha" "MIP_1beta"
## [81] "MMP_2" "MMP_3"
## [83] "MMP10" "MMP7"
## [85] "Myoglobin" "NT_proBNP"
## [87] "NrCAM" "Osteopontin"
## [89] "PAI_1" "PAPP_A"
## [91] "PLGF" "PYY"
## [93] "Pancreatic_polypeptide" "Prolactin"
## [95] "Prostatic_Acid_Phosphatase" "Protein_S"
## [97] "Pulmonary_and_Activation_Regulat" "RANTES"
## [99] "Resistin" "S100b"
## [101] "SGOT" "SHBG"
## [103] "SOD" "Serum_Amyloid_P"
## [105] "Sortilin" "Stem_Cell_Factor"
## [107] "TGF_alpha" "TIMP_1"
## [109] "TNF_RII" "TRAIL_R3"
## [111] "TTR_prealbumin" "Tamm_Horsfall_Protein_THP"
## [113] "Thrombomodulin" "Thrombopoietin"
## [115] "Thymus_Expressed_Chemokine_TECK" "Thyroid_Stimulating_Hormone"
## [117] "Thyroxine_Binding_Globulin" "Tissue_Factor"
## [119] "Transferrin" "Trefoil_Factor_3_TFF3"
## [121] "VCAM_1" "VEGF"
## [123] "Vitronectin" "von_Willebrand_Factor"
## [125] "E4" "E3"
## [127] "E2"
##
## $problemType
## [1] "Classification"
##
## $tuneValue
## fL usekernel adjust
## 2 0 TRUE 1
##
## $obsLevels
## [1] "Impaired" "Control"
## attr(,"ordered")
## [1] FALSE
##
## $param
## list()
##
## attr(,"class")
## [1] "NaiveBayes"NB_FULL_Tune$results## usekernel fL adjust ROC Sens Spec Accuracy Kappa
## 1 FALSE 0 1 0.7333788 0.6089286 0.7363158 0.7006410 0.3162972
## 2 TRUE 0 1 0.7390414 0.5821429 0.7518421 0.7043549 0.3064041
## ROCSD SensSD SpecSD AccuracySD KappaSD
## 1 0.1214378 0.2417062 0.1481122 0.1188615 0.2321727
## 2 0.1170132 0.2432769 0.1230640 0.1032011 0.2332805(NB_FULL_Train_ROCCurveAUC <- NB_FULL_Tune$results[NB_FULL_Tune$results$usekernel==NB_FULL_Tune$bestTune$usekernel,
c("ROC")])## [1] 0.7390414##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
NB_FULL_Test <- data.frame(NB_FULL_Observed = PMA_PreModelling_Test$Class,
NB_FULL_Predicted = predict(NB_FULL_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
NB_FULL_Test_ROC <- roc(response = NB_FULL_Test$NB_FULL_Observed,
predictor = NB_FULL_Test$NB_FULL_Predicted.Impaired,
levels = rev(levels(NB_FULL_Test$NB_FULL_Observed)))
(NB_FULL_Test_ROCCurveAUC <- auc(NB_FULL_Test_ROC)[1])## [1] 0.68055561.5.4 Logistic Regression Without RFE (LR_FULL)
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
[A] The logistic regression model from the stats package was implemented without recursive feature elimination through the caret package.
[B] The model does not contain any hyperparameter.
[C] The cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration is fixed due to the absence of a hyperparameter
[C.2] ROC Curve AUC = 0.70733
[D] The model allows for ranking of predictors in terms of variable importance. The top-performing predictors in the model are as follows:
[D.1] Cortisol variable (numeric)
[D.2] Apolipoprotein_A1 variable (numeric)
[D.3] MCP_2 variable (numeric)
[D.4] SOD variable (numeric)
[D.5] Fatty_Acid_Binding_Protein variable (numeric)
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.77199
##################################
# Running the logistic regression model
# by setting the caret method to 'glm'
##################################
set.seed(12345678)
LR_FULL_Tune <- caret::train(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
method = "glm",
metric = "ROC",
trControl = KFold_TrainControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
LR_FULL_Tune## Generalized Linear Model
##
## 267 samples
## 127 predictors
## 2 classes: 'Impaired', 'Control'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 240, 239, 241, 240, 241, 241, ...
## Resampling results:
##
## ROC Sens Spec Accuracy Kappa
## 0.7073308 0.5785714 0.7626316 0.7121998 0.3195053LR_FULL_Tune$finalModel##
## Call: NULL
##
## Coefficients:
## (Intercept) ACE_CD143_Angiotensin_Converti
## 388.06112 18.32099
## ACTH_Adrenocorticotropic_Hormon AXL
## -12.20991 -3.45911
## Adiponectin Alpha_1_Antichymotrypsin
## 2.32446 14.80819
## Alpha_1_Antitrypsin Alpha_1_Microglobulin
## 1.16444 -36.94175
## Alpha_2_Macroglobulin Angiopoietin_2_ANG_2
## 0.15646 -20.14245
## Angiotensinogen Apolipoprotein_A_IV
## 15.06727 -5.74660
## Apolipoprotein_A1 Apolipoprotein_A2
## 65.72600 27.28822
## Apolipoprotein_B Apolipoprotein_CI
## -3.55107 -30.47327
## Apolipoprotein_CIII Apolipoprotein_D
## -11.37076 -16.53209
## Apolipoprotein_E Apolipoprotein_H
## 9.03550 -6.17399
## B_Lymphocyte_Chemoattractant_BL BMP_6
## 14.70988 -10.29325
## Beta_2_Microglobulin Betacellulin
## 40.61790 -0.49887
## C_Reactive_Protein CD40
## -8.22874 -27.50460
## CD5L Calbindin
## -4.14634 -0.61489
## Calcitonin CgA
## 2.92788 0.05034
## Clusterin_Apo_J Complement_3
## -63.28970 0.04768
## Complement_Factor_H Connective_Tissue_Growth_Factor
## 11.86463 -40.70041
## Cortisol Creatine_Kinase_MB
## -3.89047 13.21449
## Cystatin_C EGF_R
## 49.19879 1.26933
## EN_RAGE ENA_78
## -0.13827 133.68090
## Eotaxin_3 FAS
## -0.21024 20.80350
## FSH_Follicle_Stimulation_Hormon Fas_Ligand
## 28.17017 2.03818
## Fatty_Acid_Binding_Protein Ferritin
## -20.21994 3.11387
## Fetuin_A Fibrinogen
## -64.75020 -3.78236
## GRO_alpha Gamma_Interferon_induced_Monokin
## -119.57078 36.33644
## Glutathione_S_Transferase_alpha HB_EGF
## 14.56088 3.55343
## HCC_4 Hepatocyte_Growth_Factor_HGF
## -10.83309 -66.62893
## I_309 ICAM_1
## 34.30972 0.33368
## IGF_BP_2 IL_11
## -3.23874 0.05103
## IL_13 IL_16
## -197.21721 -3.36357
## IL_17E IL_1alpha
## 5.23042 -6.53111
## IL_3 IL_4
## -1.35182 4.18246
## IL_5 IL_6
## 17.16602 3.03230
## IL_6_Receptor IL_7
## -9.96758 5.91157
## IL_8 IP_10_Inducible_Protein_10
## -59.02266 16.16684
## IgA Insulin
## 2.72264 -31.22159
## Kidney_Injury_Molecule_1_KIM_1 LOX_1
## 108.41963 10.84601
## Leptin Lipoprotein_a
## 22.72144 5.35564
## MCP_1 MCP_2
## 6.80700 -20.31103
## MIF MIP_1alpha
## 3.02394 6.99887
## MIP_1beta MMP_2
## -16.77569 -10.02623
## MMP_3 MMP10
## -2.94442 -18.03783
## MMP7 Myoglobin
## -2.01746 3.74592
## NT_proBNP NrCAM
## -21.39214 52.65218
## Osteopontin PAI_1
## -40.34846 -33.94504
## PAPP_A PLGF
## 9.86823 8.89101
## PYY Pancreatic_polypeptide
## -29.52340 -2.33873
## Prolactin Prostatic_Acid_Phosphatase
## -15.99473 -22.41759
## Protein_S Pulmonary_and_Activation_Regulat
## 27.50578 -19.51313
## RANTES Resistin
## 54.11654 0.71871
## S100b SGOT
## 33.96157 11.46625
## SHBG SOD
## 4.30953 -88.42860
## Serum_Amyloid_P Sortilin
## 8.34080 -9.71997
## Stem_Cell_Factor TGF_alpha
## 36.95818 -13.10609
## TIMP_1 TNF_RII
## 5.55468 -58.39814
## TRAIL_R3 TTR_prealbumin
## -22.47826 26.23334
## Tamm_Horsfall_Protein_THP Thrombomodulin
## -253.91847 45.05837
## Thrombopoietin Thymus_Expressed_Chemokine_TECK
## -53.44490 -2.59744
## Thyroid_Stimulating_Hormone Thyroxine_Binding_Globulin
## -5.79247 -26.53832
## Tissue_Factor Transferrin
## 3.95909 35.13989
## Trefoil_Factor_3_TFF3 VCAM_1
## 13.94217 -11.77541
## VEGF Vitronectin
## 10.17582 27.52795
## von_Willebrand_Factor E4
## 9.14893 0.93128
## E3 E2
## 3.42334 9.53014
##
## Degrees of Freedom: 266 Total (i.e. Null); 139 Residual
## Null Deviance: 313.3
## Residual Deviance: 6.654e-09 AIC: 256LR_FULL_Tune$results## parameter ROC Sens Spec Accuracy Kappa ROCSD
## 1 none 0.7073308 0.5785714 0.7626316 0.7121998 0.3195053 0.06575141
## SensSD SpecSD AccuracySD KappaSD
## 1 0.1453649 0.08268725 0.06290438 0.141295(LR_FULL_Train_ROCCurveAUC <- LR_FULL_Tune$results[,c("ROC")])## [1] 0.7073308##################################
# Identifying and plotting the
# best model predictors
##################################
LR_FULL_VarImp <- varImp(LR_FULL_Tune, scale = TRUE)
plot(LR_FULL_VarImp,
top=25,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Logistic Regression",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
LR_FULL_Test <- data.frame(LR_FULL_Observed = PMA_PreModelling_Test$Class,
LR_FULL_Predicted = predict(LR_FULL_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
LR_FULL_Test_ROC <- roc(response = LR_FULL_Test$LR_FULL_Observed,
predictor = LR_FULL_Test$LR_FULL_Predicted.Impaired,
levels = rev(levels(LR_FULL_Test$LR_FULL_Observed)))
(LR_FULL_Test_ROCCurveAUC <- auc(LR_FULL_Test_ROC)[1])## [1] 0.77199071.5.5 Support Vector Machine - Radial Basis Function Kernel Without RFE (SVM_R_FULL)
Support Vector Machine plots each observation in an N-dimensional space corresponding to the number of features in the data set and finds a hyperplane that maximally separates the different classes by a maximally large margin (which is defined as the distance between the hyperplane and the closest data points from each class). The algorithm applies kernel transformation by mapping non-linearly separable data using the similarities between the points in a high-dimensional feature space for improved discrimination.
[A] The support vector machine (radial basis function kernel) model from the kernlab package was implemented without recursive feature elimination through the caret package.
[B] The model contains 2 hyperparameters:
[B.1] sigma = sigma held constant at a value of 0.00455
[B.2] C = cost made to vary across a range of 10 default values
[C] The cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves sigma=0.00455 and C=8
[C.2] ROC Curve AUC = 0.85690
[D] The model does not allow for ranking of predictors in terms of variable importance.
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.82060
##################################
# Running the support vector machine (radial basis function kernel) model
# by setting the caret method to 'svmRadial'
##################################
set.seed(12345678)
SVM_R_FULL_Tune <- caret::train(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
method = "svmRadial",
metric = "ROC",
tuneLength = 10,
preProc = c("center", "scale"),
trControl = KFold_TrainControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
SVM_R_FULL_Tune## Support Vector Machines with Radial Basis Function Kernel
##
## 267 samples
## 127 predictors
## 2 classes: 'Impaired', 'Control'
##
## Pre-processing: centered (127), scaled (127)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 240, 239, 241, 240, 241, 241, ...
## Resampling results across tuning parameters:
##
## C ROC Sens Spec Accuracy Kappa
## 0.25 0.8156015 0.5660714 0.8913158 0.8021164 0.4673266
## 0.50 0.8156015 0.5660714 0.8913158 0.8021164 0.4673266
## 1.00 0.8218233 0.5500000 0.9018421 0.8059727 0.4665589
## 2.00 0.8366494 0.5392857 0.9328947 0.8249186 0.4988373
## 4.00 0.8524436 0.4803571 0.9484211 0.8213573 0.4700460
## 8.00 0.8569079 0.4946429 0.9378947 0.8173687 0.4713105
## 16.00 0.8555263 0.4517857 0.9428947 0.8092593 0.4385548
## 32.00 0.8555263 0.4660714 0.9431579 0.8133801 0.4515947
## 64.00 0.8555263 0.5089286 0.9378947 0.8209300 0.4850754
## 128.00 0.8555263 0.4785714 0.9171053 0.7982804 0.4292607
##
## Tuning parameter 'sigma' was held constant at a value of 0.004554698
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.004554698 and C = 8.SVM_R_FULL_Tune$finalModel## Support Vector Machine object of class "ksvm"
##
## SV type: C-svc (classification)
## parameter : cost C = 8
##
## Gaussian Radial Basis kernel function.
## Hyperparameter : sigma = 0.00455469842134126
##
## Number of Support Vectors : 199
##
## Objective Function Value : -215.1357
## Training error : 0
## Probability model included.SVM_R_FULL_Tune$results## sigma C ROC Sens Spec Accuracy Kappa
## 1 0.004554698 0.25 0.8156015 0.5660714 0.8913158 0.8021164 0.4673266
## 2 0.004554698 0.50 0.8156015 0.5660714 0.8913158 0.8021164 0.4673266
## 3 0.004554698 1.00 0.8218233 0.5500000 0.9018421 0.8059727 0.4665589
## 4 0.004554698 2.00 0.8366494 0.5392857 0.9328947 0.8249186 0.4988373
## 5 0.004554698 4.00 0.8524436 0.4803571 0.9484211 0.8213573 0.4700460
## 6 0.004554698 8.00 0.8569079 0.4946429 0.9378947 0.8173687 0.4713105
## 7 0.004554698 16.00 0.8555263 0.4517857 0.9428947 0.8092593 0.4385548
## 8 0.004554698 32.00 0.8555263 0.4660714 0.9431579 0.8133801 0.4515947
## 9 0.004554698 64.00 0.8555263 0.5089286 0.9378947 0.8209300 0.4850754
## 10 0.004554698 128.00 0.8555263 0.4785714 0.9171053 0.7982804 0.4292607
## ROCSD SensSD SpecSD AccuracySD KappaSD
## 1 0.12019683 0.2363103 0.06636648 0.06853518 0.2022444
## 2 0.12019683 0.2363103 0.06636648 0.06853518 0.2022444
## 3 0.11799422 0.2392531 0.05594882 0.06404731 0.1977729
## 4 0.10578558 0.2601129 0.04920498 0.06403128 0.2220275
## 5 0.09773967 0.2545574 0.03424874 0.07658472 0.2535540
## 6 0.09104174 0.2247842 0.04111032 0.05779809 0.1972180
## 7 0.09317267 0.2084268 0.03897443 0.05440848 0.1858882
## 8 0.09317267 0.2219927 0.03823998 0.05976578 0.2011546
## 9 0.09317267 0.2246580 0.04801700 0.07321576 0.2297742
## 10 0.09317267 0.1919216 0.04999615 0.05274999 0.1644813(SVM_R_FULL_Train_ROCCurveAUC <- SVM_R_FULL_Tune$results[SVM_R_FULL_Tune$results$C==SVM_R_FULL_Tune$bestTune$C,
c("ROC")])## [1] 0.8569079##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
SVM_R_FULL_Test <- data.frame(SVM_R_FULL_Observed = PMA_PreModelling_Test$Class,
SVM_R_FULL_Predicted = predict(SVM_R_FULL_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
SVM_R_FULL_Test_ROC <- roc(response = SVM_R_FULL_Test$SVM_R_FULL_Observed,
predictor = SVM_R_FULL_Test$SVM_R_FULL_Predicted.Impaired,
levels = rev(levels(SVM_R_FULL_Test$SVM_R_FULL_Observed)))
(SVM_R_FULL_Test_ROCCurveAUC <- auc(SVM_R_FULL_Test_ROC)[1])## [1] 0.82060191.5.6 K-Nearest Neighbors Without RFE (KNN_FULL)
K-Nearest Neighbors works on the similarity principle which assumes that every data point falling in near to each other belong to the same class. The algorithm therefore assigns an unclassified sample point the classification of the nearest of a set of previously classified points.
[A] The k-nearest neighbors model was implemented without recursive feature elimination through the caret package.
[B] The model contains 1 hyperparameter:
[B.1] k = number of neighbors made to vary across a range of 10 default values
[C] The cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves k=23
[C.2] ROC Curve AUC = 0.79886
[D] The model does not allow for ranking of predictors in terms of variable importance.
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.79167
##################################
# Running the k-nearest neighbors model
# by setting the caret method to 'knn'
##################################
set.seed(12345678)
KNN_FULL_Tune <- caret::train(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
method = "knn",
metric = "ROC",
tuneLength = 10,
preProc = c("center", "scale"),
trControl = KFold_TrainControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
KNN_FULL_Tune## k-Nearest Neighbors
##
## 267 samples
## 127 predictors
## 2 classes: 'Impaired', 'Control'
##
## Pre-processing: centered (127), scaled (127)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 240, 239, 241, 240, 241, 241, ...
## Resampling results across tuning parameters:
##
## k ROC Sens Spec Accuracy Kappa
## 5 0.6983788 0.3910714 0.8555263 0.7277066 0.2512403
## 7 0.7346217 0.3607143 0.8865789 0.7423891 0.2720487
## 9 0.7408318 0.3196429 0.9278947 0.7609381 0.2829851
## 11 0.7322462 0.3196429 0.9126316 0.7500916 0.2669541
## 13 0.7658271 0.3160714 0.9534211 0.7794770 0.3195973
## 15 0.7627961 0.2642857 0.9484211 0.7612129 0.2527805
## 17 0.7790202 0.2482143 0.9586842 0.7647843 0.2455628
## 19 0.7988322 0.2500000 0.9589474 0.7649267 0.2490637
## 21 0.7975752 0.2642857 0.9794737 0.7836081 0.2974036
## 23 0.7988651 0.2214286 0.9744737 0.7687831 0.2380964
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was k = 23.KNN_FULL_Tune$finalModel## 23-nearest neighbor model
## Training set outcome distribution:
##
## Impaired Control
## 73 194KNN_FULL_Tune$results## k ROC Sens Spec Accuracy Kappa ROCSD SensSD
## 1 5 0.6983788 0.3910714 0.8555263 0.7277066 0.2512403 0.1691065 0.2954025
## 2 7 0.7346217 0.3607143 0.8865789 0.7423891 0.2720487 0.1571726 0.2204471
## 3 9 0.7408318 0.3196429 0.9278947 0.7609381 0.2829851 0.1495604 0.1953343
## 4 11 0.7322462 0.3196429 0.9126316 0.7500916 0.2669541 0.1437392 0.1414740
## 5 13 0.7658271 0.3160714 0.9534211 0.7794770 0.3195973 0.1186339 0.1588405
## 6 15 0.7627961 0.2642857 0.9484211 0.7612129 0.2527805 0.1254370 0.1838120
## 7 17 0.7790202 0.2482143 0.9586842 0.7647843 0.2455628 0.1166273 0.1949711
## 8 19 0.7988322 0.2500000 0.9589474 0.7649267 0.2490637 0.1221173 0.1874764
## 9 21 0.7975752 0.2642857 0.9794737 0.7836081 0.2974036 0.1217746 0.1838120
## 10 23 0.7988651 0.2214286 0.9744737 0.7687831 0.2380964 0.1149696 0.2025909
## SpecSD AccuracySD KappaSD
## 1 0.07537176 0.12825365 0.3645445
## 2 0.07874682 0.10527834 0.2954716
## 3 0.05564751 0.07468878 0.2398829
## 4 0.05897190 0.05920734 0.1581750
## 5 0.05168818 0.04777909 0.1595109
## 6 0.05928942 0.06024148 0.1921446
## 7 0.04756376 0.05033696 0.1912615
## 8 0.04050694 0.05296725 0.1931351
## 9 0.02651387 0.05660022 0.2105880
## 10 0.03578367 0.06781507 0.2476633(KNN_FULL_Train_ROCCurveAUC <- KNN_FULL_Tune$results[KNN_FULL_Tune$results$k==KNN_FULL_Tune$bestTune$k,
c("ROC")])## [1] 0.7988651##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
KNN_FULL_Test <- data.frame(KNN_FULL_Observed = PMA_PreModelling_Test$Class,
KNN_FULL_Predicted = predict(KNN_FULL_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
KNN_FULL_Test_ROC <- roc(response = KNN_FULL_Test$KNN_FULL_Observed,
predictor = KNN_FULL_Test$KNN_FULL_Predicted.Impaired,
levels = rev(levels(KNN_FULL_Test$KNN_FULL_Observed)))
(KNN_FULL_Test_ROCCurveAUC <- auc(KNN_FULL_Test_ROC)[1])## [1] 0.79166671.5.7 Random Forest With RFE (RF_RFE)
Random Forest is an ensemble learning method made up of a large set of small decision trees called estimators, with each producing its own prediction. The random forest model aggregates the predictions of the estimators to produce a more accurate prediction. The algorithm involves bootstrap aggregating (where smaller subsets of the training data are repeatedly subsampled with replacement), random subspacing (where a subset of features are sampled and used to train each individual estimator), estimator training (where unpruned decision trees are formulated for each estimator) and inference by aggregating the predictions of all estimators.
Recursive Feature Elimination is a wrapper-style feature selection algorithm which searches for a subset of features by starting with all features in the training data set and successfully removing features until the desired number remains. The algorithm repeatedly fits a given machine learning algorithm used in the core of the model, ranks features by importance, discards the least important features, and re-fits the model. Features are scored either using importance scores relevant to the provided machine learning model or by applying statistical methods.
[A] The random forest model from the randomForest package was implemented with recursive feature elimination through the caret package.
[B] The model contains 1 hyperparameter:
[B.1] mtry = number of randomly selected predictors held constant at a value of 6
[C] Recursive feature elimination was applied across a range of variable subset sizes ranging from 1 to 127:
[C.1] The variable subset with the best cross-validated performance was 41 with the top 5 variables identified as:
[C.1.1] MMP10 variable (numeric)
[C.1.2] Cystatin_C variable (numeric)
[C.1.3] TRAIL_R3 variable (numeric)
[C.1.4] PAI_1 variable (numeric)
[C.1.5] GRO_alpha variable (numeric)
[D] The cross-validated model performance of the final model is summarized as follows:
[D.1] Final model configuration involves mtry=11 and variable subset=41
[D.2] ROC Curve AUC = 0.81534
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.84201
##################################
# Running the random forest model
# by setting the caret method to 'rf'
# with implementation of recursive feature elimination
##################################
KFold_RFEControl$functions <- rfFuncs
KFold_RFEControl$functions$summary <- FiveMetricsSummary
set.seed(12345678)
RF_RFE_Tune <- caret::rfe(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
sizes = VariableSubset,
metric = "ROC",
ntree = 100,
rfeControl = KFold_RFEControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
RF_RFE_Tune##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (10 fold)
##
## Resampling performance over subset size:
##
## Variables ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD
## 1 0.5659 0.3143 0.8287 0.6883 0.1559 0.1200 0.1935 0.12183
## 21 0.7939 0.3625 0.9432 0.7839 0.3501 0.1300 0.1882 0.05190
## 41 0.8153 0.2786 0.9589 0.7733 0.2720 0.1111 0.2618 0.05246
## 61 0.7913 0.3339 0.9692 0.7954 0.3590 0.1326 0.2064 0.04330
## 81 0.8069 0.3357 0.9589 0.7881 0.3402 0.1237 0.2354 0.05246
## 101 0.7845 0.3446 0.9639 0.7950 0.3566 0.1215 0.2257 0.04232
## 121 0.7938 0.3161 0.9379 0.7686 0.2903 0.1082 0.2115 0.05845
## 127 0.7805 0.2518 0.9587 0.7652 0.2488 0.1221 0.2066 0.04058
## AccuracySD KappaSD Selected
## 0.09918 0.2561
## 0.05994 0.2040
## 0.08411 0.2996 *
## 0.07272 0.2420
## 0.07653 0.2597
## 0.05929 0.2232
## 0.05791 0.2021
## 0.06769 0.2405
##
## The top 5 variables (out of 41):
## MMP10, Cystatin_C, TRAIL_R3, PAI_1, GRO_alphaRF_RFE_Tune$fit##
## Call:
## randomForest(x = x, y = y, ntree = 100, importance = TRUE)
## Type of random forest: classification
## Number of trees: 100
## No. of variables tried at each split: 6
##
## OOB estimate of error rate: 21.35%
## Confusion matrix:
## Impaired Control class.error
## Impaired 32 41 0.56164384
## Control 16 178 0.08247423RF_RFE_Tune$results## Variables ROC Sens Spec Accuracy Kappa ROCSD
## 1 1 0.5659234 0.3142857 0.8286842 0.6882784 0.1558500 0.1199506
## 2 21 0.7938534 0.3625000 0.9431579 0.7838726 0.3500941 0.1299852
## 3 41 0.8153477 0.2785714 0.9589474 0.7733109 0.2719906 0.1111441
## 4 61 0.7912758 0.3339286 0.9692105 0.7954212 0.3590385 0.1325685
## 5 81 0.8068515 0.3357143 0.9589474 0.7881359 0.3401538 0.1237378
## 6 101 0.7844901 0.3446429 0.9639474 0.7949939 0.3566195 0.1214807
## 7 121 0.7938252 0.3160714 0.9378947 0.7686304 0.2903298 0.1082441
## 8 127 0.7804652 0.2517857 0.9586842 0.7652015 0.2487579 0.1220921
## SensSD SpecSD AccuracySD KappaSD
## 1 0.1934661 0.12182850 0.09917501 0.2561487
## 2 0.1882402 0.05189840 0.05994328 0.2040432
## 3 0.2617965 0.05245878 0.08411383 0.2995929
## 4 0.2063769 0.04329549 0.07271831 0.2419879
## 5 0.2353713 0.05245878 0.07653029 0.2597450
## 6 0.2257279 0.04232490 0.05929101 0.2231779
## 7 0.2114646 0.05845292 0.05790773 0.2021318
## 8 0.2066171 0.04058001 0.06768834 0.2404506(RF_RFE_Train_ROCCurveAUC <- RF_RFE_Tune$results[RF_RFE_Tune$results$ROC==max(RF_RFE_Tune$results$ROC),
c("ROC")])## [1] 0.8153477##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
RF_RFE_Test <- data.frame(RF_RFE_Observed = PMA_PreModelling_Test$Class,
RF_RFE_Predicted = predict(RF_RFE_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
RF_RFE_Test_ROC <- roc(response = RF_RFE_Test$RF_RFE_Observed,
predictor = RF_RFE_Test$RF_RFE_Predicted.Impaired,
levels = rev(levels(RF_RFE_Test$RF_RFE_Observed)))
(RF_RFE_Test_ROCCurveAUC <- auc(RF_RFE_Test_ROC)[1])## [1] 0.84201391.5.8 Linear Discriminant Analysis With RFE (LDA_RFE)
Linear Discriminant Analysis finds a linear combination of features that best separates the classes in a data set by projecting the data onto a lower-dimensional space that maximizes the separation between the classes. The algorithm searches for a set of linear discriminants that maximize the ratio of between-class variance to within-class variance by evaluating directions in the feature space that best separate the different classes of data. LDA assumes that the data has a Gaussian distribution and that the covariance matrices of the different classes are equal, in addition to the data being linearly separable by the presence of a linear decision boundary can accurately classify the different classes.
Recursive Feature Elimination is a wrapper-style feature selection algorithm which searches for a subset of features by starting with all features in the training data set and successfully removing features until the desired number remains. The algorithm repeatedly fits a given machine learning algorithm used in the core of the model, ranks features by importance, discards the least important features, and re-fits the model. Features are scored either using importance scores relevant to the provided machine learning model or by applying statistical methods.
[A] The linear discriminant analysis model from the MASS package was implemented with recursive feature elimination through the caret package.
[B] The model does not contain any hyperparameter.
[C] Recursive feature elimination was applied across a range of variable subset sizes ranging from 1 to 127:
[C.1] The variable subset with the best cross-validated performance was 81 with the top 5 variables identified as:
[C.1.1] MMP10 variable (numeric)
[C.1.2] GRO_alpha variable (numeric)
[C.1.3] TRAIL_R3 variable (numeric)
[C.1.4] Fibrinogen variable (numeric)
[C.1.5] PAI_1 variable (numeric)
[D] The cross-validated model performance of the final model is summarized as follows:
[D.1] Final model configuration involves variable subset=81
[D.2] ROC Curve AUC = 0.83569
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.85301
##################################
# Running the linear discriminant analysis model
# by setting the caret method to 'lda'
# with implementation of recursive feature elimination
##################################
KFold_RFEControl$functions <- ldaFuncs
KFold_RFEControl$functions$summary <- FiveMetricsSummary
set.seed(12345678)
LDA_RFE_Tune <- caret::rfe(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
sizes = VariableSubset,
metric = "ROC",
tol = 1.0e-12,
rfeControl = KFold_RFEControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
LDA_RFE_Tune##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (10 fold)
##
## Resampling performance over subset size:
##
## Variables ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD
## 1 0.7185 0.1536 0.9537 0.7343 0.1316 0.09278 0.12576 0.03870
## 21 0.7659 0.4714 0.8908 0.7756 0.3780 0.13447 0.24313 0.07164
## 41 0.8337 0.6357 0.8913 0.8212 0.5283 0.10420 0.24946 0.05791
## 61 0.8338 0.6054 0.8916 0.8131 0.5147 0.07940 0.17220 0.07928
## 81 0.8357 0.6304 0.8647 0.8012 0.4961 0.06676 0.14484 0.06700
## 101 0.7910 0.6018 0.8342 0.7711 0.4318 0.06160 0.10103 0.06039
## 121 0.8175 0.6304 0.8037 0.7564 0.4166 0.05993 0.09185 0.07279
## 127 0.8015 0.6429 0.8142 0.7675 0.4377 0.07919 0.12959 0.05583
## AccuracySD KappaSD Selected
## 0.03391 0.1362
## 0.07717 0.2363
## 0.08662 0.2503
## 0.08582 0.2162
## 0.04516 0.1091 *
## 0.04707 0.1051
## 0.05361 0.1054
## 0.05778 0.1371
##
## The top 5 variables (out of 81):
## MMP10, GRO_alpha, TRAIL_R3, Fibrinogen, PAI_1LDA_RFE_Tune$fit## Call:
## lda(x, y, tol = 1e-12)
##
## Prior probabilities of groups:
## Impaired Control
## 0.2734082 0.7265918
##
## Group means:
## MMP10 GRO_alpha TRAIL_R3 Fibrinogen PAI_1 MMP7
## Impaired -3.404275 1.399543 -0.4163806 -7.090535 0.275440319 -3.156452
## Control -3.721335 1.370087 -0.5856845 -7.456059 0.002927648 -4.027631
## NT_proBNP MIF Pancreatic_polypeptide FAS Eotaxin_3
## Impaired 4.720840 -1.724998 0.3059723 -0.3996292 64.00000
## Control 4.488264 -1.916871 -0.1333379 -0.5778808 55.97938
## Gamma_Interferon_induced_Monokin Thymus_Expressed_Chemokine_TECK
## Impaired 2.829613 4.152730
## Control 2.769219 3.733299
## TNF_RII Alpha_1_Antitrypsin IGF_BP_2 Creatine_Kinase_MB MCP_2
## Impaired -0.4725251 -12.54310 5.384293 -1.702831 2.104018
## Control -0.6396312 -13.24365 5.291718 -1.663646 1.780639
## Resistin Cortisol E4 Fatty_Acid_Binding_Protein
## Impaired -15.66720 13.42740 1.589041 1.614389
## Control -18.38319 11.44124 1.329897 1.254534
## Pulmonary_and_Activation_Regulat VEGF CD5L Cystatin_C
## Impaired -1.368849 16.49858 0.05894109 8.472553
## Control -1.532822 17.17183 -0.09525496 8.628643
## HCC_4 Complement_3 Alpha_1_Microglobulin
## Impaired -3.394734 -14.85689 -2.812275
## Control -3.539563 -15.89379 -2.977260
## Hepatocyte_Growth_Factor_HGF Osteopontin
## Impaired 0.2821109 5.313664
## Control 0.1639794 5.162882
## B_Lymphocyte_Chemoattractant_BL PLGF Alpha_1_Antichymotrypsin
## Impaired 2.191212 4.012400 1.461656
## Control 1.952112 3.874869 1.322478
## MIP_1alpha Sortilin IL_7 Apolipoprotein_D S100b
## Impaired 4.281621 4.101578 2.525637 1.534726 1.339447
## Control 3.961372 3.757482 2.957174 1.404988 1.217084
## Apolipoprotein_CIII Fetuin_A IP_10_Inducible_Protein_10 IgA
## Impaired -2.402152 1.434358 5.875371 -5.928985
## Control -2.528993 1.318421 5.709495 -6.193464
## Protein_S Thrombomodulin Fas_Ligand Clusterin_Apo_J Adiponectin
## Impaired -2.161885 -1.448956 3.240515 2.952344 -5.062015
## Control -2.268898 -1.526102 2.865447 2.855998 -5.252821
## HB_EGF Thrombopoietin Alpha_2_Macroglobulin VCAM_1 MCP_1
## Impaired 7.177725 -0.8002665 -149.8084 2.750947 6.548438
## Control 6.703511 -0.7368538 -161.9286 2.663773 6.477030
## IL_8 Calbindin Apolipoprotein_B SHBG Angiotensinogen
## Impaired 1.713003 23.27885 -5.290980 -2.373962 2.358407
## Control 1.701045 22.11405 -5.686514 -2.515326 2.302900
## RANTES Kidney_Injury_Molecule_1_KIM_1 TIMP_1 I_309
## Impaired -6.453900 -1.179252 12.17891 3.023617
## Control -6.532627 -1.186509 11.58805 2.933276
## Apolipoprotein_H MMP_3 Ferritin Myoglobin SOD
## Impaired -0.2462583 -2.324019 2.931628 -1.246804 5.389265
## Control -0.3494279 -2.491243 2.701751 -1.412393 5.316217
## Thyroxine_Binding_Globulin MIP_1beta ICAM_1 MMP_2
## Impaired -1.413281 2.877145 -0.5474443 2.781498
## Control -1.503474 2.790792 -0.6071264 2.910655
## Prostatic_Acid_Phosphatase Angiopoietin_2_ANG_2 Beta_2_Microglobulin
## Impaired -1.673206 0.7189962 0.2010301
## Control -1.689756 0.6556583 0.1549796
## E2 IL_17E Leptin von_Willebrand_Factor
## Impaired 1.082192 4.671603 -1.544007 -3.848044
## Control 1.190722 4.923843 -1.489273 -3.927726
## ACTH_Adrenocorticotropic_Hormon C_Reactive_Protein Lipoprotein_a
## Impaired -1.512663 -6.014766 -4.277014
## Control -1.547922 -5.820661 -4.470063
##
## Coefficients of linear discriminants:
## LD1
## MMP10 -0.6778495578
## GRO_alpha -5.8437118089
## TRAIL_R3 -0.6406081642
## Fibrinogen -0.2338360045
## PAI_1 -0.8288542918
## MMP7 -0.0647822072
## NT_proBNP -1.3006873141
## MIF -0.6160160807
## Pancreatic_polypeptide -0.3090868060
## FAS -0.0217693800
## Eotaxin_3 -0.0081983194
## Gamma_Interferon_induced_Monokin 1.3299434140
## Thymus_Expressed_Chemokine_TECK -0.0379784241
## TNF_RII -2.0226999320
## Alpha_1_Antitrypsin -0.0923019597
## IGF_BP_2 0.3528864700
## Creatine_Kinase_MB -1.4732172451
## MCP_2 -0.2703185599
## Resistin -0.0022397928
## Cortisol -0.0556646218
## E4 -0.1667885539
## Fatty_Acid_Binding_Protein -0.4987214377
## Pulmonary_and_Activation_Regulat -0.2718329526
## VEGF 0.5807820405
## CD5L -0.1114035816
## Cystatin_C 1.7422669646
## HCC_4 0.0516411901
## Complement_3 -0.0292668839
## Alpha_1_Microglobulin -0.1467706659
## Hepatocyte_Growth_Factor_HGF -0.8418183018
## Osteopontin -0.6631915643
## B_Lymphocyte_Chemoattractant_BL 0.0358254216
## PLGF -0.0188978088
## Alpha_1_Antichymotrypsin 0.4087597274
## MIP_1alpha -0.0198960923
## Sortilin 0.1028037448
## IL_7 0.2580105214
## Apolipoprotein_D -0.0009247194
## S100b 0.4332472881
## Apolipoprotein_CIII 0.3843202615
## Fetuin_A -0.6985063910
## IP_10_Inducible_Protein_10 0.0281079434
## IgA 0.1446875319
## Protein_S 0.7699018297
## Thrombomodulin 1.0022010369
## Fas_Ligand 0.0494800814
## Clusterin_Apo_J -2.1319265640
## Adiponectin -0.2105422918
## HB_EGF 0.0225477869
## Thrombopoietin -1.0566751745
## Alpha_2_Macroglobulin 0.0102508716
## VCAM_1 0.1011044898
## MCP_1 0.1938058549
## IL_8 2.2639303597
## Calbindin -0.0237902831
## Apolipoprotein_B -0.0864330504
## SHBG 0.4018868518
## Angiotensinogen -0.6517305684
## RANTES 1.0839214997
## Kidney_Injury_Molecule_1_KIM_1 2.0403487925
## TIMP_1 -0.0447283524
## I_309 1.3638576115
## Apolipoprotein_H 0.2782456208
## MMP_3 -0.0679830388
## Ferritin -0.1086841200
## Myoglobin 0.1733153003
## SOD -0.6356300096
## Thyroxine_Binding_Globulin 0.0970457989
## MIP_1beta -0.3645585898
## ICAM_1 -0.2155873261
## MMP_2 -0.1528205683
## Prostatic_Acid_Phosphatase 1.3130124532
## Angiopoietin_2_ANG_2 -0.4535763314
## Beta_2_Microglobulin 1.5619497473
## E2 0.3302873155
## IL_17E -0.0366544919
## Leptin 0.8891369897
## von_Willebrand_Factor 0.5191263748
## ACTH_Adrenocorticotropic_Hormon 0.2546402469
## C_Reactive_Protein -0.1502267622
## Lipoprotein_a -0.0047476341LDA_RFE_Tune$results## Variables ROC Sens Spec Accuracy Kappa ROCSD
## 1 1 0.7185197 0.1535714 0.9536842 0.7343000 0.1316428 0.09278394
## 2 21 0.7658788 0.4714286 0.8907895 0.7756207 0.3779921 0.13446771
## 3 41 0.8336842 0.6357143 0.8913158 0.8212047 0.5282533 0.10419508
## 4 61 0.8338158 0.6053571 0.8915789 0.8131054 0.5146507 0.07940192
## 5 81 0.8356861 0.6303571 0.8647368 0.8011701 0.4960627 0.06676256
## 6 101 0.7909539 0.6017857 0.8342105 0.7710928 0.4317545 0.06159587
## 7 121 0.8175376 0.6303571 0.8036842 0.7564001 0.4166117 0.05993414
## 8 127 0.8015132 0.6428571 0.8142105 0.7675417 0.4377465 0.07919456
## SensSD SpecSD AccuracySD KappaSD
## 1 0.12576298 0.03870002 0.03391046 0.1362022
## 2 0.24313126 0.07164310 0.07716516 0.2363455
## 3 0.24946087 0.05790869 0.08661968 0.2502905
## 4 0.17219776 0.07927612 0.08582429 0.2162227
## 5 0.14483991 0.06700285 0.04516015 0.1091275
## 6 0.10103279 0.06039268 0.04706861 0.1050703
## 7 0.09184815 0.07279328 0.05361003 0.1053563
## 8 0.12959251 0.05582698 0.05778037 0.1370938(LDA_RFE_Train_ROCCurveAUC <- LDA_RFE_Tune$results[LDA_RFE_Tune$results$ROC==max(LDA_RFE_Tune$results$ROC),
c("ROC")])## [1] 0.8356861##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
LDA_RFE_Test <- data.frame(LDA_RFE_Observed = PMA_PreModelling_Test$Class,
LDA_RFE_Predicted = predict(LDA_RFE_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
LDA_RFE_Test_ROC <- roc(response = LDA_RFE_Test$LDA_RFE_Observed,
predictor = LDA_RFE_Test$LDA_RFE_Predicted.Impaired,
levels = rev(levels(LDA_RFE_Test$LDA_RFE_Observed)))
(LDA_RFE_Test_ROCCurveAUC <- auc(LDA_RFE_Test_ROC)[1])## [1] 0.85300931.5.9 Naive Bayes With RFE (NB_RFE)
Naive Bayes Classifier categorizes instances by applying Bayes Theorem in determining posterior probabilities as conditioned by the likelihood of features, and prior probabilities pertaining to both events and features. The algorithm naively assumes independence between features and assigns the same weight (degree of significance) to all given features.
Recursive Feature Elimination is a wrapper-style feature selection algorithm which searches for a subset of features by starting with all features in the training data set and successfully removing features until the desired number remains. The algorithm repeatedly fits a given machine learning algorithm used in the core of the model, ranks features by importance, discards the least important features, and re-fits the model. Features are scored either using importance scores relevant to the provided machine learning model or by applying statistical methods.
[A] The naive bayes model from the klaR package was implemented with recursive feature elimination through the caret package.
[B] The model contains 3 hyperparameters:
[B.1] fL = laplace correction held constant at a value of 0
[B.2] adjust = bandwidth adjustment held constant at a value of TRUE
[B.3] usekernel = distribution type made to vary across a range of levels equal to TRUE and FALSE
[C] Recursive feature elimination was applied across a range of variable subset sizes ranging from 1 to 127:
[C.1] The variable subset with the best cross-validated performance was 21 with the top 5 variables identified as:
[C.1.1] MMP10 variable (numeric)
[C.1.2] GRO_alpha variable (numeric)
[C.1.3] TRAIL_R3 variable (numeric)
[C.1.4] Fibrinogen variable (numeric)
[C.1.5] PAI_1 variable (numeric)
[D] The cross-validated model performance of the final model is summarized as follows:
[D.1] Final model configuration involves fL=0, adjust=TRUE, usekernel=TRUE and variable subset=21
[D.2] ROC Curve AUC = 0.76825
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.75231
##################################
# Running the naive bayes model
# by setting the caret method to 'nb'
# with implementation of recursive feature elimination
##################################
KFold_RFEControl$functions <- nbFuncs
KFold_RFEControl$functions$summary <- FiveMetricsSummary
set.seed(12345678)
NB_RFE_Tune <- caret::rfe(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
sizes = VariableSubset,
metric = "ROC",
rfeControl = KFold_RFEControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
NB_RFE_Tune##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (10 fold)
##
## Resampling performance over subset size:
##
## Variables ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD
## 1 0.7191 0.2089 0.9439 0.7425 0.1842 0.08707 0.1539 0.04367
## 21 0.7682 0.6107 0.7521 0.7126 0.3373 0.10731 0.2147 0.13773
## 41 0.7474 0.6232 0.7621 0.7234 0.3630 0.11767 0.2137 0.14673
## 61 0.7524 0.6375 0.7624 0.7276 0.3760 0.10482 0.2146 0.14897
## 81 0.7438 0.6232 0.7521 0.7162 0.3498 0.10873 0.2241 0.14828
## 101 0.7434 0.6232 0.7626 0.7237 0.3605 0.10817 0.2241 0.13800
## 121 0.7444 0.5946 0.7574 0.7120 0.3257 0.11227 0.2192 0.11968
## 127 0.7390 0.5821 0.7518 0.7044 0.3064 0.11701 0.2433 0.12306
## AccuracySD KappaSD Selected
## 0.05824 0.1962
## 0.10835 0.2126 *
## 0.11474 0.2210
## 0.12326 0.2361
## 0.11875 0.2276
## 0.11294 0.2227
## 0.09692 0.2052
## 0.10320 0.2333
##
## The top 5 variables (out of 21):
## MMP10, GRO_alpha, TRAIL_R3, Fibrinogen, PAI_1NB_RFE_Tune$fit## $apriori
## grouping
## Impaired Control
## 0.2734082 0.7265918
##
## $tables
## $tables$MMP10
## $tables$MMP10$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1366
##
## x y
## Min. :-5.343 Min. :0.0004514
## 1st Qu.:-4.457 1st Qu.:0.0326836
## Median :-3.570 Median :0.1318017
## Mean :-3.570 Mean :0.2817411
## 3rd Qu.:-2.684 3rd Qu.:0.4412923
## Max. :-1.798 Max. :1.1138765
##
## $tables$MMP10$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1146
##
## x y
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## 1st Qu.:-4.313 1st Qu.:0.0639944
## Median :-3.615 Median :0.1885299
## Mean :-3.615 Mean :0.3579394
## 3rd Qu.:-2.918 3rd Qu.:0.7262746
## Max. :-2.220 Max. :0.9703164
##
##
## $tables$GRO_alpha
## $tables$GRO_alpha$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.01539
##
## x y
## Min. :1.263 Min. : 0.004096
## 1st Qu.:1.332 1st Qu.: 0.543326
## Median :1.402 Median : 2.722113
## Mean :1.402 Mean : 3.594376
## 3rd Qu.:1.471 3rd Qu.: 6.180870
## Max. :1.541 Max. :10.701419
##
## $tables$GRO_alpha$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.01113
##
## x y
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## 1st Qu.:1.300 1st Qu.: 0.969942
## Median :1.363 Median : 3.052944
## Mean :1.363 Mean : 3.998520
## 3rd Qu.:1.425 3rd Qu.: 7.206995
## Max. :1.488 Max. :10.080751
##
##
## $tables$TRAIL_R3
## $tables$TRAIL_R3$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.08757
##
## x y
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## 1st Qu.:-0.7402 1st Qu.:0.0607016
## Median :-0.3161 Median :0.3646131
## Mean :-0.3161 Mean :0.5888709
## 3rd Qu.: 0.1080 3rd Qu.:1.1517577
## Max. : 0.5321 Max. :1.6025082
##
## $tables$TRAIL_R3$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.071
##
## x y
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## 1st Qu.:-0.96810 1st Qu.:0.0299330
## Median :-0.51251 Median :0.2244368
## Mean :-0.51251 Mean :0.5481931
## 3rd Qu.:-0.05692 3rd Qu.:1.1332933
## Max. : 0.39867 Max. :1.6608826
##
##
## $tables$Fibrinogen
## $tables$Fibrinogen$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2039
##
## x y
## Min. :-9.352 Min. :0.0003021
## 1st Qu.:-8.340 1st Qu.:0.0297846
## Median :-7.327 Median :0.1178889
## Mean :-7.327 Mean :0.2466951
## 3rd Qu.:-6.315 3rd Qu.:0.4610479
## Max. :-5.303 Max. :0.7651905
##
## $tables$Fibrinogen$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1616
##
## x y
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## 1st Qu.:-8.359 1st Qu.:0.0386625
## Median :-7.358 Median :0.1269579
## Mean :-7.358 Mean :0.2497188
## 3rd Qu.:-6.358 3rd Qu.:0.4814026
## Max. :-5.358 Max. :0.7053784
##
##
## $tables$PAI_1
## $tables$PAI_1$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1684
##
## x y
## Min. :-1.3796 Min. :0.0003716
## 1st Qu.:-0.6169 1st Qu.:0.0434754
## Median : 0.1458 Median :0.2050729
## Mean : 0.1458 Mean :0.3274468
## 3rd Qu.: 0.9085 3rd Qu.:0.6656947
## Max. : 1.6713 Max. :0.7852475
##
## $tables$PAI_1$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1155
##
## x y
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## 1st Qu.:-0.665516 1st Qu.:0.0466148
## Median : 0.006326 Median :0.2555443
## Mean : 0.006326 Mean :0.3717397
## 3rd Qu.: 0.678169 3rd Qu.:0.6264542
## Max. : 1.350011 Max. :1.1007939
##
##
## $tables$MMP7
## $tables$MMP7$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.4298
##
## x y
## Min. :-7.8961 Min. :0.0001895
## 1st Qu.:-5.7017 1st Qu.:0.0296584
## Median :-3.5072 Median :0.0703880
## Mean :-3.5072 Mean :0.1138034
## 3rd Qu.:-1.3127 3rd Qu.:0.2120591
## Max. : 0.8818 Max. :0.3286257
##
## $tables$MMP7$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.4545
##
## x y
## Min. :-9.761 Min. :5.093e-05
## 1st Qu.:-7.035 1st Qu.:7.612e-03
## Median :-4.310 Median :7.742e-02
## Mean :-4.310 Mean :9.163e-02
## 3rd Qu.:-1.584 3rd Qu.:1.514e-01
## Max. : 1.141 Max. :2.709e-01
##
##
## $tables$NT_proBNP
## $tables$NT_proBNP$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1278
##
## x y
## Min. :3.488 Min. :0.0004842
## 1st Qu.:4.183 1st Qu.:0.0474230
## Median :4.879 Median :0.1647061
## Mean :4.879 Mean :0.3590951
## 3rd Qu.:5.574 3rd Qu.:0.6212857
## Max. :6.270 Max. :1.2360329
##
## $tables$NT_proBNP$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.09443
##
## x y
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## 1st Qu.:3.609 1st Qu.:0.0258146
## Median :4.323 Median :0.1532264
## Mean :4.323 Mean :0.3497230
## 3rd Qu.:5.037 3rd Qu.:0.5792426
## Max. :5.751 Max. :1.3542228
##
##
## $tables$MIF
## $tables$MIF$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1116
##
## x y
## Min. :-2.7318 Min. :0.000552
## 1st Qu.:-2.1761 1st Qu.:0.050600
## Median :-1.6204 Median :0.186149
## Mean :-1.6204 Mean :0.449453
## 3rd Qu.:-1.0648 3rd Qu.:0.997236
## Max. :-0.5091 Max. :1.177368
##
## $tables$MIF$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.09496
##
## x y
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## 1st Qu.:-2.5133 1st Qu.:0.032731
## Median :-1.8945 Median :0.273283
## Mean :-1.8945 Mean :0.403567
## 3rd Qu.:-1.2756 3rd Qu.:0.735915
## Max. :-0.6567 Max. :1.257071
##
##
## $tables$Pancreatic_polypeptide
## $tables$Pancreatic_polypeptide$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2937
##
## x y
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## 1st Qu.:-0.9124 1st Qu.:0.034616
## Median : 0.3293 Median :0.179068
## Mean : 0.3293 Mean :0.201128
## 3rd Qu.: 1.5710 3rd Qu.:0.321305
## Max. : 2.8126 Max. :0.521579
##
## $tables$Pancreatic_polypeptide$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.2106
##
## x y
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## Median :-0.2358 Median :0.1182623
## Mean :-0.2358 Mean :0.1985185
## 3rd Qu.: 1.0223 3rd Qu.:0.3752603
## Max. : 2.2803 Max. :0.5975853
##
##
## $tables$FAS
## $tables$FAS$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.112
##
## x y
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## Mean :-0.3567 Mean :0.4852608
## 3rd Qu.: 0.1580 3rd Qu.:0.8077443
## Max. : 0.6726 Max. :1.3569340
##
## $tables$FAS$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.08631
##
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## Max. : 0.3542 Max. :1.4125738
##
##
## $tables$Eotaxin_3
## $tables$Eotaxin_3$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 4.556
##
## x y
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## Mean : 65.000 Mean :8.973e-03
## 3rd Qu.: 92.834 3rd Qu.:1.493e-02
## Max. :120.668 Max. :2.990e-02
##
## $tables$Eotaxin_3$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 4.684
##
## x y
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## 3rd Qu.: 80.776 3rd Qu.:1.858e-02
## Max. :110.052 Max. :2.366e-02
##
##
## $tables$Gamma_Interferon_induced_Monokin
## $tables$Gamma_Interferon_induced_Monokin$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.03894
##
## x y
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## Mean :2.840 Mean :1.458628
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##
## $tables$Gamma_Interferon_induced_Monokin$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.03562
##
## x y
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## Mean :2.707 Mean :1.189120
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## Max. :3.127 Max. :3.380559
##
##
## $tables$Thymus_Expressed_Chemokine_TECK
## $tables$Thymus_Expressed_Chemokine_TECK$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.2555
##
## x y
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##
## $tables$Thymus_Expressed_Chemokine_TECK$Control
##
## Call:
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##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1889
##
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## Max. :6.7920 Max. :0.5972220
##
##
## $tables$TNF_RII
## $tables$TNF_RII$Impaired
##
## Call:
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##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.1287
##
## x y
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##
## $tables$TNF_RII$Control
##
## Call:
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##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.09976
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##
##
## $tables$Alpha_1_Antitrypsin
## $tables$Alpha_1_Antitrypsin$Impaired
##
## Call:
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##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.4782
##
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##
## $tables$Alpha_1_Antitrypsin$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.4461
##
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##
##
## $tables$IGF_BP_2
## $tables$IGF_BP_2$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.08533
##
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## Max. :6.204 Max. :1.6631547
##
## $tables$IGF_BP_2$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.05889
##
## x y
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##
##
## $tables$Creatine_Kinase_MB
## $tables$Creatine_Kinase_MB$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.02774
##
## x y
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## Max. :-1.376 Max. :5.174907
##
## $tables$Creatine_Kinase_MB$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.02971
##
## x y
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## Median :-1.628 Median :0.921181
## Mean :-1.628 Mean :1.498501
## 3rd Qu.:-1.461 3rd Qu.:2.713159
## Max. :-1.294 Max. :4.651081
##
##
## $tables$MCP_2
## $tables$MCP_2$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.258
##
## x y
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## 1st Qu.: 0.9194 1st Qu.:0.0271061
## Median : 2.2122 Median :0.0880462
## Mean : 2.2122 Mean :0.1931765
## 3rd Qu.: 3.5050 3rd Qu.:0.3591884
## Max. : 4.7978 Max. :0.6187589
##
## $tables$MCP_2$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1411
##
## x y
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## 1st Qu.: 1.09474 1st Qu.:0.0145562
## Median : 2.21217 Median :0.1158586
## Mean : 2.21217 Mean :0.2234860
## 3rd Qu.: 3.32960 3rd Qu.:0.3215262
## Max. : 4.44703 Max. :0.8589345
##
##
## $tables$Resistin
## $tables$Resistin$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 2.203
##
## x y
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## Max. : 4.370 Max. :7.088e-02
##
## $tables$Resistin$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 1.737
##
## x y
## Min. :-37.350 Min. :1.397e-05
## 1st Qu.:-27.539 1st Qu.:2.853e-03
## Median :-17.728 Median :1.742e-02
## Mean :-17.728 Mean :2.546e-02
## 3rd Qu.: -7.917 3rd Qu.:4.772e-02
## Max. : 1.895 Max. :6.884e-02
##
##
## $tables$Cortisol
## $tables$Cortisol$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 1.139
##
## x y
## Min. : 0.5829 Min. :5.461e-05
## 1st Qu.: 8.5415 1st Qu.:3.538e-03
## Median :16.5000 Median :9.519e-03
## Mean :16.5000 Mean :3.138e-02
## 3rd Qu.:24.4585 3rd Qu.:4.563e-02
## Max. :32.4171 Max. :1.343e-01
##
## $tables$Cortisol$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 1.095
##
## x y
## Min. :-3.185 Min. :6.347e-05
## 1st Qu.: 3.933 1st Qu.:5.143e-03
## Median :11.050 Median :1.131e-02
## Mean :11.050 Mean :3.509e-02
## 3rd Qu.:18.167 3rd Qu.:6.471e-02
## Max. :25.285 Max. :1.184e-01
##
##
## $tables$E4
## $tables$E4$Impaired
##
## Call:
## density.default(x = xx)
##
## Data: xx (73 obs.); Bandwidth 'bw' = 0.189
##
## x y
## Min. :0.4329 Min. :0.009735
## 1st Qu.:0.9664 1st Qu.:0.114497
## Median :1.5000 Median :0.380075
## Mean :1.5000 Mean :0.467481
## 3rd Qu.:2.0336 3rd Qu.:0.781582
## Max. :2.5671 Max. :1.242688
##
## $tables$E4$Control
##
## Call:
## density.default(x = xx)
##
## Data: xx (194 obs.); Bandwidth 'bw' = 0.1479
##
## x y
## Min. :0.5562 Min. :0.008458
## 1st Qu.:1.0281 1st Qu.:0.071644
## Median :1.5000 Median :0.346103
## Mean :1.5000 Mean :0.528563
## 3rd Qu.:1.9719 3rd Qu.:0.834386
## Max. :2.4438 Max. :1.806543
##
##
##
## $levels
## [1] "Impaired" "Control"
##
## $call
## NaiveBayes.default(x = x, grouping = y, usekernel = TRUE, fL = 2)
##
## $x
## MMP10 GRO_alpha TRAIL_R3 Fibrinogen PAI_1 MMP7 NT_proBNP
## 1 -3.270169 1.381830 -0.18290044 -7.035589 1.00350156 -3.7735027 4.553877
## 2 -3.649659 1.372438 -0.50074709 -8.047190 -0.03059880 -5.9681907 4.219508
## 3 -2.733368 1.412679 -0.92403445 -7.195437 0.43837211 -4.0302269 4.248495
## 5 -2.617296 1.398431 -0.85825911 -6.980326 0.25230466 -0.2222222 4.465908
## 6 -3.324236 1.398431 -0.73800921 -6.437752 0.43837211 -1.9223227 4.189655
## 7 -4.135167 1.338425 -0.62997381 -7.621105 0.00000000 -5.9681907 4.330733
## 8 -3.688879 1.350892 -0.56347899 -6.502290 0.49054798 -2.4721360 3.828641
## 9 -4.017384 1.381830 -0.75712204 -7.902008 -0.47754210 -5.8446454 5.043425
## 11 -3.963316 1.412679 -0.37116408 -7.523941 0.25230466 -3.7735027 4.875197
## 12 -3.244194 1.398431 -0.68264012 -7.278819 0.25230466 -3.0000000 4.727388
## 14 -3.575551 1.440955 -0.54746226 -6.991137 0.32004747 -1.3806170 4.691348
## 16 -3.123566 1.412679 -0.48559774 -7.222466 0.49054798 -4.0302269 5.323010
## 17 -3.411248 1.419083 0.00000000 -6.319969 0.32004747 -2.8507125 4.595120
## 18 -3.963316 1.324552 -0.75712204 -7.402052 0.32004747 -1.2879797 3.931826
## 19 -4.074542 1.405814 -0.41274719 -6.959049 0.53887915 -3.3452248 4.290459
## 20 -2.563950 1.430692 -0.85825911 -5.843045 0.85893499 -0.6037782 3.784190
## 21 -3.324236 1.398431 0.26936976 -7.182192 -0.65480247 -3.3452248 5.262690
## 22 -3.611918 1.405814 -0.20634242 -7.385791 -0.15428707 -4.0302269 4.828314
## 23 -4.135167 1.338425 -0.56347899 -7.641724 -0.04107298 -6.3770782 3.663562
## 24 -3.381395 1.372438 -0.25465110 -7.600902 -0.21752413 -4.3245553 4.709530
## 25 -3.506558 1.308996 -0.70078093 -7.435388 -0.72247798 -4.0302269 4.672829
## 26 -3.381395 1.381830 -0.37116408 -7.452482 0.09396047 -3.5470020 4.499810
## 28 -3.381395 1.350892 -0.70078093 -7.323271 -0.05168998 -4.0302269 4.465908
## 29 -3.772261 1.338425 -0.83723396 -7.875339 -0.87443088 -2.2640143 3.931826
## 30 -3.863233 1.398431 -0.94693458 -7.875339 -0.14221210 -3.7735027 4.317488
## 31 -3.244194 1.381830 -0.62997381 -6.645391 0.09396047 -3.3452248 4.828314
## 34 -3.270169 1.458333 -0.13734056 -6.969631 0.58384004 -2.8507125 4.770685
## 35 -3.506558 1.362172 -0.64724718 -7.236259 0.00000000 -2.5883147 4.605170
## 36 -3.218876 1.445658 -0.68264012 -6.571283 0.00000000 -0.7216553 4.718499
## 37 -3.218876 1.398431 -0.34425042 -7.505592 0.09396047 -3.7735027 4.595120
## 38 -3.270169 1.398431 -0.56347899 -7.561682 0.25230466 -4.7040152 4.605170
## 39 -4.074542 1.291400 -0.57973042 -8.111728 0.09396047 -4.5938047 4.262680
## 40 -3.912023 1.430692 -0.47064906 -7.824046 0.32004747 -1.6514837 4.499810
## 41 -3.270169 1.398431 -0.47064906 -6.377127 0.25230466 -3.7735027 4.983607
## 42 -3.816713 1.398431 -0.73800921 -7.487574 -0.11859478 -4.3245553 4.700480
## 43 -3.218876 1.362172 -0.48559774 -7.143478 -0.28605071 -3.1639778 4.304065
## 44 -3.123566 1.362172 -0.64724718 -6.812445 0.62582535 -4.4888568 4.736198
## 45 -2.764621 1.425073 -0.21823750 -6.571283 0.17742506 -2.1702883 4.634729
## 46 -3.270169 1.372438 -0.57973042 -8.180721 0.17742506 -1.1622777 4.499810
## 47 -3.649659 1.405814 -0.64724718 -7.505592 -0.11859478 -2.8507125 4.976734
## 48 -3.816713 1.405814 -0.13734056 -8.111728 0.49054798 -4.0302269 4.919981
## 50 -2.645075 1.435976 0.00000000 -7.354042 0.17742506 -3.5470020 5.129899
## 51 -4.074542 1.338425 -0.73800921 -7.278819 -0.40885871 -4.6666667 4.795791
## 53 -3.411248 1.390462 -0.53167272 -6.907755 0.09396047 -3.7735027 4.127134
## 55 -3.540459 1.372438 -0.47064906 -7.641724 0.09396047 -6.6874449 4.127134
## 56 -3.729701 1.390462 -0.37116408 -7.208860 0.17742506 -4.3245553 5.062595
## 57 -3.442019 1.338425 -0.27956244 -7.264430 0.49054798 -2.8507125 4.574711
## 59 -3.772261 1.430692 -0.56347899 -7.082109 1.10005082 -3.0000000 5.036953
## 60 -4.074542 1.381830 -0.53167272 -6.812445 -0.27188464 -4.3887656 4.736198
## 61 -4.017384 1.362172 -0.79641472 -7.013116 -0.25795574 -3.5470020 4.488636
## 62 -4.422849 1.324552 -1.09654116 -7.662778 -0.55204550 -6.7705802 4.574711
## 63 -3.688879 1.390462 -0.33102365 -7.250246 -0.01006550 -3.7735027 4.948760
## 64 -3.101093 1.430692 -0.44133043 -7.195437 0.76993928 -1.0151134 5.181784
## 65 -3.649659 1.372438 -0.68264012 -8.254829 0.09396047 -4.0302269 4.143135
## 67 -3.772261 1.390462 -0.56347899 -7.523941 -0.16654597 -6.3045480 4.859812
## 68 -3.540459 1.308996 -0.31794508 -7.278819 -0.04107298 -4.3245553 3.610918
## 69 -4.342806 1.308996 -1.09654116 -7.957577 -0.11859478 -5.7849894 4.304065
## 70 -3.324236 1.381830 -0.39871863 -7.957577 0.17742506 -3.3452248 5.003946
## 71 -4.135167 1.350892 -0.61296931 -7.505592 0.73700033 -4.0302269 4.605170
## 72 -3.506558 1.372438 -0.21823750 -6.505132 0.09396047 -4.0302269 4.634729
## 73 -3.324236 1.338425 -0.30501103 -7.354042 0.58384004 -5.2074997 4.795791
## 74 -2.995732 1.362172 -0.90163769 -7.143478 0.49054798 -2.0000000 4.406719
## 75 -3.015935 1.430692 -0.77658561 -6.437752 0.58384004 -2.4721360 4.820282
## 76 -2.577022 1.430692 -0.30501103 -7.130899 0.73700033 -2.7140452 4.770685
## 77 -3.473768 1.462144 -0.31794508 -6.319969 0.76993928 -4.5582584 4.727388
## 78 -3.036554 1.445658 -0.10425819 -6.502290 0.83076041 -3.1639778 4.990433
## 80 -3.411248 1.338425 -0.47064906 -6.917806 0.17742506 -2.3643578 4.770685
## 81 -3.540459 1.419083 -0.62997381 -7.902008 -0.14221210 -3.7735027 4.859812
## 82 -3.473768 1.350892 -0.68264012 -7.338538 0.17742506 -4.0302269 4.406719
## 83 -3.411248 1.372438 -0.44133043 -8.804875 -0.16654597 -3.5470020 4.595120
## 84 -3.575551 1.435976 -0.54746226 -7.250246 0.32004747 -3.3452248 4.543295
## 85 -3.688879 1.362172 -0.37116408 -7.182192 0.43837211 -3.7735027 5.468060
## 86 -3.123566 1.435976 -0.30501103 -6.214608 0.25230466 -2.7140452 5.117994
## 88 -3.270169 1.430692 -0.17134851 -6.917806 0.38177502 -3.1639778 4.727388
## 90 -4.509860 1.350892 -0.81662520 -8.873868 -0.63330256 -8.3975049 3.178054
## 93 -3.688879 1.372438 -0.44133043 -6.959049 0.38177502 -3.5470020 4.317488
## 94 -2.645075 1.475713 0.00000000 -6.502290 0.80114069 -1.4299717 5.886104
## 95 -3.688879 1.405814 -0.54746226 -7.469874 0.17742506 -2.7140452 4.762174
## 96 -3.816713 1.381830 -0.54746226 -7.986565 -0.23078200 -3.3452248 4.521789
## 97 -3.649659 1.381830 -0.71923319 -7.875339 -0.05168998 -4.5938047 4.543295
## 98 -4.342806 1.324552 -0.92403445 -8.468403 -0.51401261 -7.3250481 4.477337
## 99 -3.963316 1.405814 -0.57973042 -6.812445 0.62582535 -3.5935279 4.290459
## 100 -3.649659 1.362172 -0.48559774 -7.751725 0.25230466 -5.1611487 4.634729
## 103 -2.995732 1.405814 -0.38485910 -7.799353 0.43837211 -3.2335542 4.663439
## 104 -3.963316 1.372438 -0.45589516 -7.323271 -0.17899381 -4.8199434 4.430817
## 105 -3.863233 1.372438 -0.75712204 -7.706263 -0.27188464 -5.5592895 4.454347
## 107 -3.575551 1.398431 -0.47064906 -7.208860 0.00000000 -1.3806170 4.369448
## 108 -3.912023 1.338425 -0.61296931 -7.070274 0.09396047 -1.9223227 4.682131
## 109 -4.667046 1.350892 -1.21070858 -8.517193 -0.24425708 -7.5346259 4.465908
## 110 -3.963316 1.435976 -0.75712204 -8.334872 -0.06245326 -4.3245553 4.043051
## 111 -3.575551 1.350892 -0.75712204 -7.293418 -0.47754210 -1.2879797 4.110874
## 112 -2.631089 1.425073 -0.27956244 -6.214608 0.17742506 -3.3452248 5.323010
## 113 -2.995732 1.390462 -0.42694948 -5.991465 0.70214496 -2.3643578 4.787492
## 114 -4.199705 1.381830 -0.83723396 -7.035589 -0.24425708 -5.1156807 4.543295
## 115 -3.381395 1.450108 -0.42694948 -6.437752 -0.13031621 -2.2640143 4.700480
## 117 -3.575551 1.398431 -0.42694948 -7.452482 0.38177502 -4.3564173 4.812184
## 118 -2.207275 1.435976 -0.42694948 -7.452482 0.83076041 -2.3643578 4.700480
## 121 -3.575551 1.398431 -0.13734056 -7.250246 0.53887915 -0.4253563 5.062595
## 123 -4.199705 1.350892 -0.99435191 -8.334872 -0.42552800 -2.0000000 4.304065
## 124 -3.863233 1.338425 -0.20634242 -7.182192 0.00000000 -3.1639778 4.875197
## 126 -4.268698 1.381830 -0.38485910 -7.195437 -0.19163579 -4.8199434 4.912655
## 128 -3.272534 1.454327 -0.27956244 -6.725434 0.95939061 -1.2025631 4.962845
## 129 -4.017384 1.338425 -0.70078093 -7.600902 -0.57168558 -5.9056942 4.624973
## 130 -4.017384 1.372438 -0.39871863 -6.571283 0.25230466 -3.7735027 5.159055
## 131 -3.863233 1.372438 -0.87972006 -7.418581 -0.34523643 -5.0710678 4.025352
## 132 -3.506558 1.362172 -0.62997381 -7.143478 -0.08443323 -4.0302269 4.442651
## 133 -3.912023 1.271288 -0.92403445 -5.914504 0.09396047 -2.4721360 4.672829
## 134 -2.813411 1.398431 -0.31794508 -6.812445 0.43837211 -0.5000000 4.727388
## 135 -4.017384 1.308996 -0.53167272 -7.250246 0.00000000 -4.3245553 4.584967
## 136 -3.575551 1.412679 -0.38485910 -6.907755 0.43837211 -2.4721360 4.727388
## 137 -3.963316 1.381830 -1.04412698 -7.902008 -0.01006550 -4.6299354 4.488636
## 139 -3.442019 1.338425 -0.47064906 -7.662778 -0.59176325 -3.3452248 4.543295
## 140 -3.729701 1.398431 -0.48559774 -7.250246 0.00000000 -4.0302269 4.406719
## 141 -4.074542 1.419083 -0.34425042 -6.437752 0.25230466 -3.1639778 4.820282
## 143 -3.688879 1.350892 -0.61296931 -7.561682 0.00000000 -3.5470020 4.574711
## 144 -3.912023 1.372438 -0.64724718 -7.070274 0.49054798 -3.7735027 4.276666
## 145 -4.074542 1.350892 -0.39871863 -8.180721 -0.63330256 -5.0272837 4.276666
## 146 -2.975930 1.381830 -0.53167272 -6.725434 0.43837211 -2.1702883 4.406719
## 147 -3.270169 1.412679 -0.47064906 -7.024289 0.43837211 -3.7735027 4.634729
## 148 -3.411248 1.425073 -0.94693458 -6.907755 0.09396047 -1.7139068 4.543295
## 149 -3.688879 1.381830 -0.97036428 -7.875339 -0.27188464 -1.7139068 4.290459
## 152 -3.244194 1.494568 -0.18290044 -6.571283 0.88578467 -2.0824829 4.682131
## 153 -3.473768 1.372438 -0.41274719 -8.111728 0.00000000 -3.5470020 4.672829
## 154 -4.422849 1.308996 -0.85825911 -7.542634 -0.36070366 -5.9681907 3.806662
## 155 -3.963316 1.372438 -0.75712204 -7.986565 -0.24425708 -5.2547625 4.442651
## 156 -3.506558 1.390462 -0.61296931 -6.991137 0.43837211 -1.1622777 4.369448
## 157 -3.912023 1.324552 -0.42694948 -7.957577 1.00350156 -3.1639778 4.770685
## 158 -3.575551 1.350892 -0.81662520 -7.799353 -0.51401261 -5.1611487 3.828641
## 159 -4.074542 1.450108 -0.75712204 -6.917806 0.17742506 -4.3245553 4.859812
## 160 -3.540459 1.350892 -0.59622443 -7.452482 -0.07336643 -4.5582584 4.454347
## 161 -3.963316 1.419083 -0.30501103 -7.706263 -0.69936731 -5.0710678 5.081404
## 162 -4.074542 1.405814 -0.42694948 -7.751725 -0.07336643 -3.7735027 4.406719
## 163 -4.509860 1.390462 -0.75712204 -7.561682 -0.57168558 -6.6874449 4.262680
## 165 -3.473768 1.390462 -0.44133043 -7.402052 0.25230466 -3.5470020 4.499810
## 166 -4.074542 1.350892 -0.79641472 -7.728736 -0.31512364 -5.4023321 4.624973
## 167 -2.864704 1.372438 -0.30501103 -7.875339 -0.42552800 -1.5921060 4.605170
## 168 -3.772261 1.350892 -0.62997381 -7.418581 -0.42552800 -0.9814240 4.025352
## 169 -4.342806 1.324552 -0.51610326 -7.182192 0.00000000 -2.0000000 4.465908
## 170 -4.933674 1.372438 -0.47064906 -7.013116 -0.10704332 -2.0000000 4.499810
## 171 -3.270169 1.381830 -0.38485910 -7.323271 -0.45985790 -4.9421013 4.948760
## 172 -4.074542 1.381830 -0.62997381 -7.323271 -0.65480247 -5.2074997 4.510860
## 174 -3.101093 1.412679 -0.51610326 -8.421883 0.17742506 -1.5921060 4.595120
## 175 -2.343407 1.435976 -0.31794508 -5.914504 0.58384004 -2.5883147 4.584967
## 176 -3.912023 1.419083 -0.56347899 -8.873868 0.32004747 -5.0710678 5.003946
## 177 -3.473768 1.338425 -0.68264012 -8.016418 0.25230466 -4.7419986 4.770685
## 178 -3.688879 1.412679 -0.51610326 -7.799353 -0.82104815 -4.0302269 4.682131
## 179 -2.659260 1.398431 -0.54746226 -7.323271 -0.01006550 -2.1884251 4.983607
## 180 -3.649659 1.398431 -0.21823750 -7.369791 -0.10704332 -2.0000000 4.727388
## 181 -4.135167 1.291400 -0.97036428 -8.254829 0.17742506 -5.5592895 4.382027
## 182 -3.912023 1.324552 -0.61296931 -7.435388 0.49054798 -5.0710678 4.553877
## 183 -3.611918 1.372438 -0.68264012 -6.645391 0.17742506 -3.7735027 4.406719
## 184 -3.473768 1.372438 -0.37116408 -7.561682 0.38177502 -5.4023321 4.653960
## 185 -3.324236 1.435976 -0.15990607 -6.812445 0.49054798 -4.4549722 4.948760
## 186 -3.688879 1.308996 -0.30501103 -7.208860 -0.08443323 -5.0272837 4.304065
## 189 -3.772261 1.338425 -0.68264012 -7.487574 0.09396047 -6.0321933 4.672829
## 190 -3.912023 1.291400 -0.47064906 -7.293418 0.32004747 -4.0302269 4.174387
## 191 -3.101093 1.308996 -0.68264012 -7.338538 -0.63330256 -6.8561489 4.700480
## 192 -3.473768 1.425073 -0.38485910 -7.684284 0.25230466 -4.3245553 4.382027
## 193 -3.473768 1.405814 -0.47064906 -7.278819 1.00350156 -3.5470020 5.283204
## 194 -3.688879 1.308996 -0.70078093 -7.469874 0.17742506 -2.8507125 4.787492
## 195 -3.473768 1.412679 -0.47064906 -7.523941 0.43837211 -1.1234752 4.488636
## 197 -3.575551 1.405814 -0.06149412 -7.118476 -0.25795574 -2.2640143 5.204007
## 198 -4.135167 1.350892 -0.79641472 -7.418581 -0.82104815 -5.5058663 4.077537
## 200 -3.575551 1.372438 -0.79641472 -7.662778 0.00000000 -4.0302269 4.812184
## 201 -3.540459 1.381830 -0.38485910 -7.561682 -0.06245326 -3.3452248 4.204693
## 202 -4.074542 1.308996 -0.79641472 -8.294050 -0.49558921 -6.6066297 4.204693
## 205 -3.411248 1.390462 -0.59622443 -7.487574 0.32004747 -2.0000000 4.369448
## 208 -3.816713 1.291400 -0.62997381 -6.948577 -0.27188464 -5.0710678 4.382027
## 210 -3.170086 1.398431 -0.21823750 -7.106206 0.53887915 -3.0000000 5.241747
## 212 -4.342806 1.390462 -0.71923319 -7.621105 -0.20447735 -4.0302269 4.644391
## 213 -3.101093 1.405814 -0.41274719 -6.812445 -0.08443323 -3.0000000 4.836282
## 214 -3.218876 1.462144 -0.71923319 -7.013116 1.16610855 -3.3452248 4.304065
## 215 -3.912023 1.324552 -0.68264012 -7.824046 -0.39250510 -1.6514837 4.430817
## 216 -3.473768 1.425073 -0.38485910 -6.119298 0.25230466 -4.4549722 3.663562
## 218 -3.381395 1.398431 -0.51610326 -6.571283 0.66516665 -2.2640143 4.204693
## 219 -4.199705 1.412679 -0.68264012 -7.684284 -0.15428707 -5.1611487 4.787492
## 220 -3.816713 1.390462 -0.71923319 -7.849364 -0.65480247 -5.8446454 4.897840
## 223 -3.575551 1.338425 -0.62997381 -6.917806 -0.51401261 -3.3452248 4.828314
## 224 -3.912023 1.308996 -0.70078093 -7.264430 0.00000000 -5.8446454 4.304065
## 225 -4.135167 1.372438 -0.34425042 -7.418581 0.17742506 -5.5058663 4.394449
## 226 -3.575551 1.398431 -0.41274719 -7.024289 0.43837211 -3.0000000 4.442651
## 227 -3.649659 1.398431 -0.50074709 -6.214608 0.00000000 -3.0000000 4.077537
## 228 -3.963316 1.362172 -0.71923319 -7.070274 -0.63330256 -1.4299717 4.553877
## 229 -3.270169 1.381830 -0.57973042 -7.621105 -0.21752413 -0.4077171 4.248495
## 230 -3.123566 1.390462 -0.42694948 -7.293418 0.43837211 -2.7140452 4.691348
## 231 -3.912023 1.381830 -0.51610326 -6.907755 0.00000000 -4.4888568 4.595120
## 232 -4.135167 1.362172 -0.42694948 -7.143478 0.00000000 -5.3521462 4.143135
## 233 -3.688879 1.398431 -0.90163769 -7.469874 -0.24425708 -5.3029674 4.043051
## 234 -3.352407 1.271288 -0.56347899 -7.250246 -0.61229604 -3.3452248 4.521789
## 236 -3.575551 1.324552 -0.56347899 -7.621105 -0.65480247 -4.0302269 4.382027
## 237 -3.688879 1.390462 -0.51610326 -7.182192 -0.09565753 -4.0302269 4.574711
## 239 -4.199705 1.271288 -0.77658561 -8.217089 0.09396047 -4.0302269 4.394449
## 240 -4.017384 1.324552 -0.83723396 -8.145630 -0.42552800 -4.0302269 4.442651
## 241 -3.611918 1.338425 -0.54746226 -8.047190 0.00000000 -3.7735027 4.859812
## 242 -3.506558 1.372438 -0.35762924 -7.600902 0.25230466 -1.7796447 3.828641
## 243 -3.772261 1.362172 -0.75712204 -6.938214 0.49054798 -4.6299354 5.075174
## 244 -3.079114 1.435976 -0.41274719 -7.799353 0.85893499 -3.7735027 5.225747
## 245 -4.342806 1.390462 -0.77658561 -8.740337 -0.16654597 -6.4515425 3.871201
## 246 -3.324236 1.362172 -0.41274719 -7.208860 0.25230466 -2.7140452 4.510860
## 247 -3.863233 1.398431 -0.70078093 -8.180721 -0.57168558 -4.7806350 4.553877
## 249 -3.863233 1.398431 -0.15990607 -7.581100 0.00000000 -5.4023321 4.820282
## 250 -3.611918 1.381830 -0.50074709 -7.600902 0.09396047 -4.5582584 4.234107
## 251 -4.017384 1.362172 -0.68264012 -7.775256 -0.55204550 -4.6299354 4.553877
## 253 -3.863233 1.405814 -0.42694948 -6.502290 0.58384004 -2.7140452 4.962845
## 254 -3.036554 1.398431 0.18568645 -7.250246 0.09396047 -2.3643578 5.411646
## 255 -3.270169 1.405814 0.00000000 -8.740337 0.09396047 -2.2640143 4.875197
## 256 -4.342806 1.324552 -0.64724718 -8.334872 -0.59176325 -6.3770782 4.859812
## 257 -3.863233 1.362172 -0.64724718 -7.775256 -0.37645673 -5.7266741 4.543295
## 258 -3.270169 1.390462 -0.34425042 -6.948577 0.09396047 -3.1639778 4.553877
## 260 -3.611918 1.440955 -0.41274719 -6.938214 0.09396047 -6.6066297 4.204693
## 261 -4.342806 1.398431 -0.71923319 -8.111728 -0.07336643 -7.1287093 4.406719
## 262 -4.422849 1.390462 -0.90163769 -7.308233 -0.53282641 -5.7266741 4.077537
## 263 -3.772261 1.372438 -0.47064906 -7.561682 -0.15428707 -1.2879797 3.871201
## 264 -3.442019 1.350892 -0.06149412 -6.502290 0.17742506 -2.0824829 5.707110
## 265 -3.863233 1.350892 -0.81662520 -7.775256 0.09396047 -6.0977633 3.433987
## 267 -3.816713 1.390462 -0.18290044 -7.047017 0.66516665 -2.0000000 4.882802
## 268 -3.296837 1.362172 -0.44133043 -7.600902 -0.47754210 -3.5470020 4.465908
## 269 -4.342806 1.324552 -0.34425042 -7.875339 -0.99084860 -5.5058663 4.624973
## 270 -4.135167 1.440955 -0.41274719 -7.662778 -0.07336643 -1.8490018 4.025352
## 271 -3.352407 1.398431 -0.20634242 -6.959049 -0.02026405 -2.2640143 4.488636
## 272 -4.605170 1.350892 -0.53167272 -7.824046 -0.10704332 -4.4216130 4.564348
## 273 -3.506558 1.350892 -0.38485910 -7.222466 0.32004747 -3.0000000 4.043051
## 274 -3.963316 1.398431 -0.38485910 -7.505592 -0.03059880 -5.6696499 3.970292
## 275 -3.540459 1.338425 -0.85825911 -8.047190 0.17742506 -4.0302269 4.442651
## 277 -3.611918 1.405814 -0.48559774 -6.725434 0.53887915 -1.7139068 4.143135
## 278 -3.772261 1.324552 -0.53167272 -6.725434 -0.10704332 -3.1639778 4.663439
## 279 -4.017384 1.381830 -0.57973042 -7.706263 -0.13031621 -3.1639778 4.753590
## 281 -3.170086 1.398431 -0.21823750 -6.571283 0.70214496 -3.3452248 4.276666
## 282 -3.194183 1.419083 -0.73800921 -8.740337 0.38177502 -3.7735027 3.828641
## 283 -3.473768 1.435976 -0.29221795 -6.725434 0.43837211 -4.0302269 4.477337
## 287 -3.218876 1.372438 -0.42694948 -7.775256 0.09396047 -3.7735027 4.454347
## 289 -4.342806 1.338425 -0.92403445 -7.542634 -0.23078200 -5.3029674 3.951244
## 290 -3.649659 1.445658 -0.42694948 -7.824046 0.09396047 -3.5470020 3.610918
## 291 -2.830218 1.475713 -0.35762924 -7.058578 0.70214496 -0.9814240 4.356709
## 292 -3.381395 1.350892 -0.37116408 -7.236259 0.32004747 -4.0302269 4.779123
## 294 -3.963316 1.308996 -0.50074709 -7.662778 -0.55204550 -6.0977633 4.553877
## 297 -4.199705 1.350892 -0.64724718 -8.016418 -0.16654597 -4.3887656 4.564348
## 298 -3.963316 1.291400 -0.77658561 -7.082109 0.00000000 -5.5058663 3.951244
## 299 -3.772261 1.372438 -0.37116408 -7.581100 0.09396047 -5.1156807 4.927254
## 301 -4.268698 1.362172 -0.71923319 -8.047190 -0.17899381 -4.4549722 4.356709
## 302 -3.863233 1.271288 -0.87972006 -7.469874 -0.63330256 -4.0302269 4.343805
## 303 -4.268698 1.308996 -0.70078093 -7.156217 -0.20447735 -5.2547625 4.442651
## 304 -3.079114 1.445658 -0.29221795 -7.728736 0.62582535 -3.0000000 5.111988
## 305 -3.540459 1.390462 -0.54746226 -6.980326 0.73700033 -3.7735027 4.682131
## 306 -3.729701 1.412679 -0.48559774 -7.106206 0.38177502 -4.4216130 4.595120
## 307 -3.324236 1.372438 -0.21823750 -5.914504 0.85893499 -3.5470020 4.779123
## 308 -3.816713 1.381830 -0.56347899 -6.725434 0.09396047 -6.9442719 4.595120
## 311 -3.649659 1.412679 -0.35762924 -7.600902 -0.11859478 -3.7735027 4.770685
## 312 -3.296837 1.390462 -0.21823750 -7.293418 0.32004747 -3.7735027 4.912655
## 313 -4.074542 1.440955 -0.83723396 -7.505592 0.17742506 -5.0710678 4.317488
## 314 -3.863233 1.324552 -0.66479918 -8.334872 -0.17899381 -6.5280287 4.709530
## 315 -3.688879 1.372438 -0.56347899 -7.024289 -0.09565753 -3.7735027 4.418841
## 316 -3.688879 1.338425 -1.15193183 -7.799353 0.25230466 -5.1611487 4.290459
## 317 -3.381395 1.350892 -0.59622443 -7.581100 -0.04107298 -4.9421013 4.653960
## 320 -3.381395 1.308996 -0.75712204 -6.725434 -0.28605071 -4.6299354 4.465908
## 321 -4.199705 1.324552 -1.01892829 -7.728736 -0.10704332 -4.6666667 3.784190
## 322 -3.649659 1.398431 -0.37116408 -6.377127 0.49054798 -0.8867513 4.912655
## 323 -3.540459 1.338425 -0.31794508 -7.195437 0.32004747 -3.5470020 5.135798
## 324 -3.963316 1.271288 -0.59622443 -8.468403 0.25230466 -6.3045480 4.875197
## 325 -3.352407 1.398431 -0.68264012 -6.907755 0.85893499 -4.3245553 4.488636
## 326 -3.352407 1.398431 -0.42694948 -7.986565 0.09396047 -3.7735027 4.510860
## 327 -3.381395 1.398431 -0.41274719 -7.293418 0.53887915 -0.7472113 4.890349
## 329 -3.506558 1.405814 -0.68264012 -7.775256 0.17742506 -4.9843030 4.465908
## 330 -3.352407 1.381830 -0.77658561 -6.571283 0.09396047 -1.2025631 4.744932
## 331 -3.912023 1.372438 -1.01892829 -7.236259 -0.09565753 -6.1649658 4.304065
## 332 -3.816713 1.362172 -0.94693458 -7.024289 0.17742506 -3.7735027 4.189655
## 333 -3.772261 1.350892 -0.38485910 -7.236259 -0.53282641 -5.5058663 4.465908
## MIF Pancreatic_polypeptide FAS Eotaxin_3
## 1 -1.2378744 0.57878085 -0.08338161 53
## 2 -1.8971200 0.33647224 -0.52763274 62
## 3 -2.3025851 -0.89159812 -0.63487827 62
## 5 -1.8971200 0.26236426 -0.12783337 64
## 6 -2.0402208 -0.47803580 -0.32850407 57
## 7 -2.1202635 -0.59783700 -0.71334989 64
## 8 -1.7719568 -0.31471074 -0.71334989 64
## 9 -2.2072749 -0.52763274 -0.82098055 64
## 11 -1.5141277 -1.27296568 -0.02020271 82
## 12 -1.7147984 1.16315081 -0.71334989 73
## 14 -2.0402208 -0.37106368 -0.44628710 67
## 16 -1.5141277 0.33647224 -0.41551544 69
## 17 -1.9661129 0.78845736 -0.02020271 76
## 18 -2.3330443 -0.59783700 -0.82098055 33
## 19 -1.7147984 0.18232156 -0.47803580 54
## 20 -2.3538784 -0.26136476 -0.63487827 77
## 21 -1.4696760 0.69314718 -0.07257069 64
## 22 -1.4696760 -1.23787436 -0.30110509 73
## 23 -2.1202635 -0.82098055 -0.86750057 30
## 24 -1.7147984 -0.04082199 -0.07257069 82
## 25 -2.1202635 -1.27296568 -0.57981850 82
## 26 -1.5606477 0.09531018 -0.16251893 70
## 28 -1.8971200 0.40546511 -0.49429632 76
## 29 -1.8971200 0.09531018 -1.04982212 34
## 30 -2.4079456 0.09531018 -0.96758403 43
## 31 -2.1202635 0.33647224 -0.82098055 64
## 34 -1.8971200 0.91629073 -0.82098055 44
## 35 -1.8325815 0.53062825 -0.82098055 44
## 36 -1.8325815 -0.75502258 -0.49429632 64
## 37 -1.4696760 -0.10536052 -0.28768207 70
## 38 -1.7147984 -0.63487827 -0.82098055 34
## 39 -1.8325815 -0.71334989 -0.44628710 62
## 40 -2.1202635 -0.51082562 -0.63487827 62
## 41 -1.5141277 0.18232156 -0.52763274 54
## 42 -1.7719568 -1.27296568 -0.61618614 92
## 43 -2.2072749 0.00000000 -0.57981850 43
## 44 -1.8971200 0.47000363 -0.63487827 72
## 45 -1.3470736 0.64185389 -0.05129329 82
## 46 -1.8325815 -0.26136476 -0.44628710 72
## 47 -1.8325815 0.18232156 -0.75502258 64
## 48 -1.1711830 0.69314718 -0.30110509 96
## 50 -1.6607312 -0.41551544 -0.44628710 73
## 51 -1.9661129 -0.96758403 -0.65392647 54
## 53 -1.8971200 -0.34249031 -0.52763274 52
## 55 -2.1202635 0.26236426 -0.63487827 30
## 56 -1.8325815 -0.46203546 -0.71334989 54
## 57 -1.2729657 1.06471074 -0.11653382 49
## 59 -1.7147984 -0.32850407 -0.71334989 64
## 60 -2.2072749 0.95551145 -0.82098055 53
## 61 -2.1202635 -0.09431068 -0.71334989 43
## 62 -2.3538784 -0.73396918 -1.10866262 33
## 63 -1.5606477 0.91629073 -0.37106368 64
## 64 -1.6607312 0.83290912 -0.44628710 54
## 65 -2.0402208 0.83290912 -0.73396918 52
## 67 -1.3470736 -0.32850407 -0.44628710 54
## 68 -1.1394343 0.26236426 -0.08338161 64
## 69 -2.2072749 -0.16251893 -0.96758403 43
## 70 -1.6607312 0.26236426 -0.44628710 70
## 71 -2.1202635 0.40546511 -0.52763274 52
## 72 -1.7147984 -0.59783700 0.09531018 83
## 73 -1.6094379 0.26236426 -0.15082289 83
## 74 -2.3751558 0.53062825 -0.89159812 53
## 75 -2.3330443 0.69314718 -0.52763274 83
## 76 -1.5141277 1.02961942 -0.31471074 54
## 77 -1.5606477 0.83290912 -0.47803580 44
## 78 -1.6607312 1.52605630 0.33647224 70
## 80 -1.6094379 0.33647224 -0.52763274 53
## 81 -1.8971200 -0.63487827 -0.71334989 44
## 82 -1.8971200 0.09531018 -0.57981850 70
## 83 -1.8971200 -0.40047757 -0.26136476 44
## 84 -1.6607312 -0.63487827 -0.94160854 69
## 85 -1.7147984 -0.32850407 -0.31471074 44
## 86 -1.4696760 1.93152141 -0.31471074 78
## 88 -1.2729657 0.47000363 -0.24846136 64
## 90 -2.3025851 -0.40047757 -0.71334989 39
## 93 -1.8325815 0.18232156 -0.47803580 64
## 94 -1.4271164 1.25276297 0.09531018 83
## 95 -2.1202635 0.47000363 -0.82098055 43
## 96 -1.6094379 -0.79850770 -0.32850407 70
## 97 -2.0402208 -0.67334455 -0.57981850 70
## 98 -2.2072749 -1.02165125 -1.51412773 33
## 99 -2.1202635 0.91629073 -0.63487827 83
## 100 -1.8971200 -0.23572233 -0.71334989 39
## 103 -1.8325815 -0.19845094 -0.35667494 93
## 104 -1.5606477 0.26236426 -0.71334989 44
## 105 -1.8971200 0.26236426 -0.52763274 52
## 107 -1.6607312 -0.16251893 -0.52763274 48
## 108 -2.0402208 -0.03045921 -0.61618614 64
## 109 -2.4304185 -0.71334989 -0.94160854 41
## 110 -2.2072749 -2.12026354 -1.02165125 38
## 111 -1.4271164 -0.40047757 -0.34249031 64
## 112 -1.5606477 1.64865863 -0.18632958 59
## 113 -1.9661129 0.69314718 -0.02020271 70
## 114 -2.1202635 -0.73396918 -0.69314718 70
## 115 -1.5606477 1.09861229 -0.71334989 64
## 117 -1.8971200 0.18232156 -0.52763274 64
## 118 -1.1086626 -1.27296568 -0.26136476 95
## 121 -1.6607312 0.18232156 -0.56211892 64
## 123 -2.5510465 -0.23572233 -1.10866262 69
## 124 -1.7719568 0.09531018 -1.04982212 44
## 126 -1.8971200 -0.52763274 -0.61618614 59
## 128 -1.8971200 0.47000363 -0.31471074 44
## 129 -2.1202635 -0.75502258 -1.07880966 54
## 130 -1.9661129 0.58778666 -0.07257069 57
## 131 -2.3126354 0.78845736 -0.57981850 52
## 132 -2.2072749 -0.31471074 -0.71334989 64
## 133 -1.8325815 -0.52763274 -0.32850407 82
## 134 -1.0216512 1.25276297 0.18232156 70
## 135 -1.9661129 -0.31471074 -0.57981850 64
## 136 -2.0402208 0.69314718 -1.04982212 64
## 137 -2.2072749 -0.34249031 -0.73396918 41
## 139 -1.7719568 -0.47803580 -0.49429632 57
## 140 -1.4271164 1.19392247 -0.26136476 70
## 141 -1.8325815 0.18232156 -0.22314355 70
## 143 -2.1202635 0.99325177 -0.82098055 64
## 144 -2.0402208 0.58778666 -0.32850407 64
## 145 -2.3025851 -0.86750057 -0.96758403 33
## 146 -1.6094379 1.33500107 -0.18632958 88
## 147 -1.2378744 0.78845736 -0.32850407 70
## 148 -1.8325815 0.00000000 -0.30110509 73
## 149 -2.2072749 -1.10866262 -0.52763274 52
## 152 -0.8439701 1.13140211 0.09531018 107
## 153 -1.7147984 0.64185389 -0.18632958 95
## 154 -2.1202635 -0.71334989 -0.73396918 62
## 155 -1.8971200 -0.86750057 -0.96758403 59
## 156 -2.3126354 -0.26136476 -0.63487827 67
## 157 -1.9661129 0.26236426 -0.52763274 85
## 158 -2.0402208 -0.96758403 -0.73396918 62
## 159 -2.1202635 0.18232156 -0.71334989 23
## 160 -1.8971200 0.33647224 -0.28768207 62
## 161 -1.7147984 -1.27296568 -0.31471074 64
## 162 -1.2378744 -0.40047757 -0.30110509 49
## 163 -2.0402208 -0.96758403 -1.27296568 34
## 165 -1.6607312 -1.07880966 -0.75502258 57
## 166 -1.7719568 -0.23572233 -0.94160854 64
## 167 -1.4696760 -1.34707365 -0.18632958 64
## 168 -2.3025851 -0.82098055 -0.28768207 46
## 169 -2.1202635 -0.47803580 -0.32850407 70
## 170 -1.4696760 -0.61618614 -0.32850407 82
## 171 -1.8325815 -0.09431068 -0.44628710 54
## 172 -2.2072749 -0.16251893 -0.71334989 43
## 174 -1.7147984 0.83290912 -0.24846136 54
## 175 -1.8325815 0.33647224 -0.26136476 70
## 176 -1.7147984 0.64185389 -0.37106368 64
## 177 -1.8325815 -0.16251893 -0.57981850 44
## 178 -1.9661129 -0.99425227 -0.94160854 34
## 179 -2.3751558 -0.34249031 -0.02020271 70
## 180 -1.8971200 0.58194114 -0.12783337 82
## 181 -2.1202635 -0.47803580 -0.57981850 29
## 182 -1.8971200 -0.09431068 -0.44628710 64
## 183 -2.5133061 -1.42711636 -0.96758403 64
## 184 -1.3093333 -0.31471074 0.00000000 70
## 185 -2.0402208 0.78845736 -0.31471074 59
## 186 -1.8325815 0.64185389 -0.26136476 45
## 189 -1.6607312 1.56861592 -0.57981850 33
## 190 -1.6607312 -0.40047757 -0.38566248 54
## 191 -1.7147984 -1.96611286 -0.57981850 44
## 192 -1.4271164 1.30833282 -0.22314355 54
## 193 -1.4696760 0.87546874 -0.52763274 73
## 194 -2.0402208 -0.52763274 -0.71334989 44
## 195 -2.0402208 -0.46203546 -0.28768207 67
## 197 -0.9416085 -0.23572233 -0.26136476 82
## 198 -2.1202635 -1.23787436 -1.04982212 44
## 200 -1.6607312 -0.49429632 -0.96758403 43
## 201 -1.8325815 -0.23572233 -0.34249031 44
## 202 -2.2072749 -0.86750057 -0.96758403 48
## 205 -1.8971200 -0.41551544 -0.63487827 77
## 208 -2.0402208 0.00000000 -0.57981850 44
## 210 -2.2072749 0.47000363 -0.18632958 76
## 212 -1.8325815 1.93152141 -0.94160854 44
## 213 -1.9661129 1.19392247 -0.12783337 70
## 214 -2.0402208 0.87546874 -0.35667494 41
## 215 -1.9661129 0.47000363 -0.44628710 43
## 216 -2.1202635 -0.31471074 -0.47803580 44
## 218 -1.9661129 -0.01005034 -0.26136476 54
## 219 -1.8971200 0.87546874 -1.07880966 44
## 220 -1.8325815 -0.37106368 -0.71334989 44
## 223 -2.1202635 0.91629073 -0.61618614 43
## 224 -2.0402208 -0.73396918 -0.96758403 43
## 225 -1.8971200 -0.49429632 -0.37106368 53
## 226 -1.7147984 0.18232156 -0.15082289 83
## 227 -2.3859667 1.19392247 -0.44628710 74
## 228 -2.3025851 -0.31471074 -0.96758403 33
## 229 -2.0402208 -0.73396918 -0.57981850 45
## 230 -1.2729657 1.62924054 -0.02020271 70
## 231 -1.9661129 -0.67334455 -0.26136476 57
## 232 -1.7719568 -0.40047757 -0.86750057 54
## 233 -2.1202635 -0.63487827 -1.04982212 34
## 234 -1.8971200 0.74193734 -0.40047757 70
## 236 -2.0402208 -0.94160854 -0.40047757 57
## 237 -1.8971200 1.09861229 -0.61618614 44
## 239 -2.3859667 -0.16251893 -0.96758403 33
## 240 -2.1202635 0.18232156 -0.96758403 43
## 241 -1.6607312 -0.31471074 -0.40047757 45
## 242 -2.6736488 0.99325177 -0.67334455 72
## 243 -2.0402208 -0.02020271 -0.82098055 44
## 244 -1.7147984 0.74193734 -0.71334989 64
## 245 -2.3968958 -0.40047757 -0.47803580 23
## 246 -2.3330443 0.58778666 -0.49429632 64
## 247 -1.8325815 -0.16251893 -0.65392647 39
## 249 -1.4696760 -1.07880966 -0.05129329 82
## 250 -2.3025851 -0.82098055 -0.52763274 72
## 251 -1.8325815 0.09531018 -1.07880966 54
## 253 -1.8325815 0.33647224 -0.52763274 64
## 254 -1.8325815 -0.59783700 -0.03045921 80
## 255 -1.4271164 -0.63487827 -0.52763274 73
## 256 -1.7719568 0.53062825 -1.07880966 34
## 257 -2.3330443 -0.09431068 -0.82098055 44
## 258 -1.8971200 0.33647224 -0.35667494 72
## 260 -1.7719568 -0.69314718 -0.30110509 64
## 261 -2.3751558 0.00000000 -1.07880966 7
## 262 -1.6094379 -0.69314718 -0.86750057 39
## 263 -1.2378744 -1.13943428 -0.30110509 64
## 264 -2.0402208 0.60449978 -0.52763274 69
## 265 -1.9661129 0.91629073 -0.57981850 54
## 267 -1.4696760 0.83290912 -0.02020271 70
## 268 -1.2729657 0.40546511 -0.57981850 44
## 269 -2.2072749 -1.42711636 -0.71334989 33
## 270 -1.5606477 -0.47803580 -0.22314355 49
## 271 -1.5606477 -0.23572233 -0.30110509 64
## 272 -1.5141277 -0.23572233 -0.57981850 49
## 273 -1.3862944 0.09531018 -0.22314355 78
## 274 -1.6094379 -1.13943428 -0.47803580 39
## 275 -1.7719568 -0.49429632 -0.61618614 53
## 277 -1.9661129 0.53062825 -0.32850407 33
## 278 -2.2072749 0.18232156 -0.96758403 53
## 279 -1.8971200 -0.94160854 -0.40047757 51
## 281 -1.7719568 0.18232156 -0.07257069 82
## 282 -1.8971200 0.33647224 -0.52763274 72
## 283 -1.5545112 0.58778666 -0.38566248 92
## 287 -2.1202635 0.26236426 -0.63487827 52
## 289 -2.5383074 -0.59783700 -0.96758403 33
## 290 -2.1202635 0.33647224 -0.63487827 46
## 291 -1.4271164 -0.10536052 0.00000000 57
## 292 -1.8325815 -0.23572233 -0.37106368 74
## 294 -2.4769385 -1.02165125 -0.82098055 43
## 297 -2.0402208 0.74193734 -0.94160854 54
## 298 -2.5010360 0.09531018 -0.94160854 62
## 299 -1.4271164 -0.94160854 -0.40047757 82
## 301 -1.8325815 -0.41551544 -0.94160854 54
## 302 -2.8473123 -0.86750057 -0.96758403 33
## 303 -1.9661129 -0.16251893 -0.38566248 54
## 304 -1.6094379 0.18232156 -0.31471074 59
## 305 -1.3862944 0.09531018 -0.30110509 73
## 306 -1.7147984 0.18232156 -0.24846136 74
## 307 -1.8325815 0.99325177 -0.37106368 64
## 308 -1.7719568 0.99325177 -0.96758403 33
## 311 -2.1202635 -0.41551544 -0.71334989 54
## 312 -1.6607312 0.78845736 -0.52763274 64
## 313 -1.7719568 0.00000000 -0.71334989 54
## 314 -1.5141277 -0.96758403 -0.38566248 44
## 315 -1.9661129 -0.16251893 -0.61618614 48
## 316 -2.0402208 0.09531018 -0.57981850 41
## 317 -2.2072749 -0.34249031 -0.52763274 62
## 320 -2.3025851 0.40546511 -0.44628710 62
## 321 -2.6310892 0.26236426 -0.86750057 52
## 322 -1.8971200 -0.52763274 -0.41551544 64
## 323 -1.3862944 0.53062825 -0.15082289 54
## 324 -1.6607312 0.58778666 -0.71334989 43
## 325 -1.7719568 0.78845736 -0.47803580 54
## 326 -1.9661129 -0.04082199 -0.71334989 44
## 327 -1.1086626 0.78845736 -0.02020271 82
## 329 -1.8971200 0.33647224 -0.61618614 44
## 330 -2.5010360 0.78845736 -0.26136476 70
## 331 -1.6607312 -0.96758403 -0.71334989 49
## 332 -1.2729657 -1.34707365 -0.57981850 54
## 333 -1.3093333 -0.52763274 -0.08338161 69
## Gamma_Interferon_induced_Monokin Thymus_Expressed_Chemokine_TECK
## 1 2.949822 4.149327
## 2 2.721793 3.810182
## 3 2.762231 2.791992
## 5 2.851987 4.534163
## 6 2.822442 4.534163
## 7 2.739315 3.342694
## 8 2.966101 4.037285
## 9 2.584357 3.637051
## 11 2.701785 4.908629
## 12 2.769220 3.637051
## 14 2.924402 4.534163
## 16 2.911527 4.093428
## 17 2.845167 5.273838
## 18 2.956388 2.407182
## 19 3.019718 4.260413
## 20 2.708297 3.810182
## 21 2.929867 4.908629
## 22 2.724975 3.578777
## 23 2.568127 3.810182
## 24 2.614139 4.534163
## 25 2.667835 2.472433
## 26 2.788951 4.149327
## 28 2.680311 3.282892
## 29 2.713850 3.578777
## 30 2.766469 2.407182
## 31 2.790112 2.854653
## 34 2.883453 4.093428
## 35 2.802914 4.093428
## 36 2.848747 4.479850
## 37 2.786199 4.149327
## 38 2.919789 4.093428
## 39 2.620513 4.315608
## 40 2.876049 1.936058
## 41 2.825646 3.637051
## 42 2.603403 4.093428
## 43 2.927719 3.342694
## 44 2.908388 2.791992
## 45 2.792403 4.908629
## 46 2.762231 4.315608
## 47 2.757357 3.867347
## 48 2.879640 3.752748
## 50 2.787386 4.093428
## 51 2.829324 3.810182
## 53 2.848276 3.810182
## 55 2.637144 2.791992
## 56 2.852215 2.601557
## 57 2.864362 6.225224
## 59 2.974175 3.101492
## 60 2.936028 1.796259
## 61 2.742679 2.854653
## 62 2.684529 2.854653
## 63 2.726228 4.479850
## 64 3.065368 4.479850
## 65 2.632564 3.810182
## 67 2.674201 4.855724
## 68 2.713850 6.225224
## 69 2.732330 2.854653
## 70 2.809013 2.791992
## 71 2.780093 3.810182
## 72 2.928799 4.749337
## 73 2.939917 4.961345
## 74 2.916177 3.752748
## 75 2.939917 3.810182
## 76 2.818539 4.479850
## 77 2.946345 5.325310
## 78 2.943646 6.225224
## 80 2.735867 3.342694
## 81 2.760303 3.637051
## 82 2.757852 3.282892
## 83 2.668760 4.908629
## 84 2.774554 3.637051
## 85 2.851530 4.479850
## 86 2.851530 4.479850
## 88 2.909446 4.149327
## 90 2.668760 3.040333
## 93 2.881737 4.037285
## 94 3.032417 5.222195
## 95 2.868085 3.752748
## 96 2.827923 3.980894
## 97 2.778414 4.149327
## 98 2.694967 1.508487
## 99 2.873869 3.810182
## 100 2.722435 3.101492
## 103 2.876049 4.534163
## 104 2.705439 4.037285
## 105 2.579644 3.810182
## 107 2.752296 3.752748
## 108 2.832888 3.342694
## 109 2.646995 2.208489
## 110 2.750740 2.854653
## 111 2.636011 3.040333
## 112 2.893035 4.149327
## 113 2.875131 4.149327
## 114 2.694190 2.791992
## 115 2.845409 3.637051
## 117 2.806678 4.315608
## 118 2.968193 4.149327
## 121 3.000719 3.637051
## 123 2.706159 2.854653
## 124 2.872708 3.040333
## 126 2.715877 3.867347
## 128 2.763185 4.093428
## 129 2.690241 3.101492
## 130 2.901221 4.908629
## 131 2.532501 3.578777
## 132 2.766469 3.752748
## 133 2.786199 4.149327
## 134 2.791263 6.225224
## 135 2.893035 3.342694
## 136 2.905282 3.578777
## 137 2.568127 2.791992
## 139 2.665023 3.282892
## 140 2.860121 4.149327
## 141 2.604783 4.149327
## 143 2.767852 3.752748
## 144 2.911270 3.752748
## 145 2.674201 2.854653
## 146 2.950670 3.980894
## 147 2.858842 3.752748
## 148 2.908918 4.479850
## 149 2.790112 3.342694
## 152 2.984412 5.580204
## 153 2.731131 4.149327
## 154 2.579644 2.537220
## 155 2.691833 3.342694
## 156 2.926626 4.315608
## 157 2.903482 3.752748
## 158 2.557619 3.342694
## 159 2.845892 3.637051
## 160 2.769220 4.315608
## 161 2.843211 4.479850
## 162 2.875866 3.578777
## 163 2.678590 4.479850
## 165 2.788951 3.040333
## 166 2.662160 3.637051
## 167 2.843948 4.908629
## 168 2.681164 3.342694
## 169 2.824491 3.282892
## 170 2.881391 3.752748
## 171 2.823909 4.855724
## 172 2.694967 3.752748
## 174 2.886307 4.093428
## 175 2.786199 4.149327
## 176 2.823031 4.093428
## 177 2.760303 3.578777
## 178 2.704715 3.637051
## 179 2.891000 5.273838
## 180 3.008161 5.580204
## 181 2.596327 4.037285
## 182 2.773241 3.520211
## 183 2.823325 2.407182
## 184 2.654255 4.908629
## 185 2.771914 4.093428
## 186 2.701785 4.149327
## 189 2.777140 3.520211
## 190 2.839703 4.479850
## 191 2.715205 4.037285
## 192 2.845409 4.479850
## 193 2.852896 3.637051
## 194 2.771023 4.037285
## 195 2.708297 3.810182
## 197 2.815134 3.980894
## 198 2.743782 4.037285
## 200 2.735867 2.854653
## 201 2.785402 4.908629
## 202 2.712482 3.342694
## 205 2.557619 4.315608
## 208 2.824491 4.037285
## 210 2.919789 4.534163
## 212 2.700297 1.936058
## 213 2.754850 4.534163
## 214 2.874020 3.342694
## 215 2.656269 3.752748
## 216 2.752811 4.479850
## 218 2.890046 4.908629
## 219 2.687819 3.637051
## 220 2.594876 3.637051
## 223 2.710405 4.149327
## 224 2.735867 2.854653
## 225 2.744330 3.752748
## 226 2.768310 4.037285
## 227 2.763659 3.752748
## 228 2.653238 1.796259
## 229 2.665023 3.520211
## 230 2.963228 4.908629
## 231 2.767393 3.752748
## 232 2.697275 4.479850
## 233 2.771023 3.040333
## 234 2.715877 3.752748
## 236 2.767393 3.282892
## 237 2.763185 3.637051
## 239 2.875683 3.342694
## 240 2.791263 2.854653
## 241 2.724975 4.149327
## 242 2.654255 2.791992
## 243 2.665966 3.637051
## 244 2.783386 4.093428
## 245 2.692623 4.479850
## 246 2.883623 3.752748
## 247 2.792403 4.479850
## 249 2.665023 4.149327
## 250 2.870614 3.040333
## 251 2.737602 3.101492
## 253 3.008576 4.093428
## 254 3.011822 4.149327
## 255 2.769220 3.637051
## 256 2.393337 3.101492
## 257 2.584357 3.637051
## 258 2.906102 5.580204
## 260 2.822737 4.037285
## 261 2.695741 3.101492
## 262 2.530419 4.037285
## 263 2.807684 4.037285
## 264 2.763185 4.093428
## 265 2.611519 3.810182
## 267 3.009810 4.149327
## 268 2.906780 4.479850
## 269 2.524026 3.520211
## 270 2.735284 3.578777
## 271 2.910232 4.908629
## 272 2.735284 4.908629
## 273 2.842468 4.479850
## 274 2.785402 3.578777
## 275 2.825933 3.342694
## 277 2.943646 4.534163
## 278 2.937016 3.101492
## 279 2.788951 3.980894
## 281 2.875131 4.908629
## 282 2.900935 4.315608
## 283 2.953166 4.479850
## 287 2.666903 2.791992
## 289 2.786596 2.407182
## 290 2.701785 3.342694
## 291 2.839193 5.580204
## 292 2.848747 4.149327
## 294 2.627850 2.407182
## 297 2.751261 2.601557
## 298 2.657266 2.208489
## 299 2.788951 4.534163
## 301 2.620513 3.101492
## 302 2.584357 3.342694
## 303 2.760303 4.908629
## 304 2.700297 3.867347
## 305 2.905965 4.908629
## 306 2.732330 4.534163
## 307 2.945455 4.315608
## 308 2.781750 1.796259
## 311 2.854245 4.479850
## 312 2.810980 3.637051
## 313 2.800812 3.040333
## 314 2.568127 4.037285
## 315 2.846133 2.854653
## 316 2.603403 3.810182
## 317 2.766469 3.810182
## 320 2.666903 3.342694
## 321 2.738746 2.791992
## 322 2.863047 4.093428
## 323 2.913692 4.037285
## 324 2.847566 3.342694
## 325 3.006900 3.578777
## 326 2.864685 4.037285
## 327 2.897137 5.273838
## 329 2.749166 3.637051
## 330 2.713850 4.534163
## 331 2.678590 4.260413
## 332 2.748106 4.479850
## 333 2.862841 4.037285
## TNF_RII Alpha_1_Antitrypsin IGF_BP_2 Creatine_Kinase_MB MCP_2
## 1 -0.06187540 -12.631361 5.609472 -1.710172 1.9805094
## 2 -0.32850407 -11.909882 5.347108 -1.751002 1.8088944
## 3 -0.41551544 -13.642963 5.181784 -1.383559 0.4005958
## 5 -0.34249031 -11.133063 5.420535 -1.625834 2.2208309
## 6 -0.94160854 -12.134638 5.056246 -1.671366 2.3343863
## 7 -0.77652879 -12.813142 5.438079 -1.739232 2.1030230
## 8 -0.91629073 -13.310348 5.365976 -1.571048 2.6867663
## 9 -0.94160854 -12.907477 5.273000 -1.671366 1.8527528
## 11 -0.51082562 -13.310348 5.505332 -1.751002 4.0237466
## 12 -0.71334989 -11.838035 5.081404 -1.671366 1.5303762
## 14 -0.61618614 -11.909882 5.209486 -1.683772 2.4440754
## 16 -0.28768207 -11.983227 5.375278 -1.671366 1.0483341
## 17 -0.69314718 -11.499497 5.455321 -1.871938 2.8501989
## 18 -0.77652879 -14.135373 5.087596 -1.780911 1.8527528
## 19 -0.79850770 -12.292758 5.379897 -1.647864 2.8501989
## 20 -0.75502258 -8.932463 5.513429 -1.518336 1.5303762
## 21 -0.65392647 -14.135373 5.361292 -1.671366 2.8501989
## 22 -0.04082199 -15.344812 5.141664 -1.647864 1.7643559
## 23 -0.59783700 -13.642963 4.955827 -1.590122 1.8088944
## 24 -0.43078292 -13.310348 5.472271 -1.751002 1.0483341
## 25 -0.82098055 -13.528896 5.187386 -1.724319 2.1820549
## 26 -0.43078292 -13.205557 5.257495 -1.724319 2.0219013
## 28 -0.22314355 -11.909882 5.438079 -1.755051 1.6263611
## 29 -1.02165125 -14.135373 4.997212 -1.647864 2.2969819
## 30 -0.89159812 -13.760451 5.147494 -1.710172 2.1030230
## 31 -0.73396918 -12.058126 5.513429 -1.653590 2.5152196
## 34 -0.65392647 -11.435607 5.273000 -1.625834 2.7530556
## 35 -0.89159812 -12.458129 4.836282 -1.625834 1.5303762
## 36 -0.67334455 -11.909882 5.598422 -1.671366 1.8527528
## 37 -0.30110509 -14.548755 5.351858 -1.780911 1.6263611
## 38 -0.65392647 -13.528896 5.141664 -1.585271 1.8527528
## 39 -0.46203546 -16.321511 5.252273 -1.683772 0.4005958
## 40 -0.44628710 -13.418078 5.407172 -1.590122 2.6191813
## 41 -0.75502258 -8.191715 5.327876 -1.671366 1.0483341
## 42 -0.69314718 -13.881545 5.420535 -1.724319 1.0483341
## 43 -0.86750057 -13.004247 5.198497 -1.710172 1.8527528
## 44 -0.24846136 -12.907477 5.420535 -1.590122 1.8088944
## 45 -0.02020271 -12.058126 5.645447 -1.780911 3.0369315
## 46 -0.38566248 -15.008176 5.267858 -1.590122 1.1637797
## 47 -0.79850770 -11.630963 5.351858 -1.868851 1.8527528
## 48 -0.27443685 -15.344812 5.700444 -1.780911 1.5303762
## 50 -0.54472718 -11.698625 5.262690 -1.671366 3.2434918
## 51 -0.38566248 -12.374500 5.398163 -1.571048 1.7643559
## 53 -0.71334989 -13.881545 5.278115 -1.518336 1.1637797
## 55 -0.63487827 -15.344812 5.030438 -1.590122 1.6731213
## 56 -0.63487827 -11.698625 5.262690 -1.696685 1.5303762
## 57 -0.31471074 -12.212827 5.641907 -1.571048 1.9805094
## 59 -0.30110509 -11.250842 5.616771 -1.671366 2.5848812
## 60 -0.59783700 -13.103567 5.438079 -1.653590 1.6731213
## 61 -1.04982212 -14.006447 5.192957 -1.710172 1.5303762
## 62 -1.20397280 -15.008176 5.123964 -1.653590 1.8527528
## 63 -0.27443685 -12.907477 5.283204 -1.724319 2.3713615
## 64 -0.44628710 -12.631361 5.370638 -1.671366 2.1427912
## 65 -0.73396918 -15.523564 5.164786 -1.590122 1.1637797
## 67 -0.18632958 -14.406260 5.356586 -1.868851 1.5303762
## 68 -0.51082562 -14.268559 5.429346 -1.647864 1.7643559
## 69 -1.04982212 -15.008176 5.141664 -1.653590 1.1637797
## 70 -0.61618614 -14.406260 5.594711 -1.755051 2.0219013
## 71 -0.46203546 -12.134638 5.068904 -1.683772 1.3273591
## 72 0.33647224 -10.963846 5.780744 -1.751002 2.2591348
## 73 -0.03045921 -14.135373 5.509388 -1.459630 1.1637797
## 74 -1.34707365 -10.802885 5.241747 -1.605032 2.5152196
## 75 -0.67334455 -12.907477 5.337538 -1.631218 1.8088944
## 76 0.00000000 -10.363053 5.549076 -1.755051 1.6731213
## 77 -0.41551544 -11.564602 5.129899 -1.751002 1.5303762
## 78 -0.06187540 -11.311317 5.624018 -1.780911 2.6191813
## 80 -0.65392647 -13.205557 5.204007 -1.605032 1.8527528
## 81 -0.59783700 -12.292758 5.111988 -1.780911 0.4005958
## 82 -0.44628710 -12.292758 5.521461 -1.871938 2.0219013
## 83 -0.82098055 -12.374500 4.905275 -1.441430 2.3713615
## 84 -0.57981850 -11.564602 5.043425 -1.671366 2.1427912
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## 186 -0.82098055 -16.545310 5.198497 -1.724319 1.6263611
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## 200 -0.59783700 -12.721137 5.323010 -1.653590 1.5303762
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## 210 -0.28768207 -10.699822 5.613128 -1.780911 4.0237466
## 212 -0.89159812 -12.631361 5.187386 -1.780911 1.0483341
## 213 -0.46203546 -12.458129 5.407172 -1.590122 1.6263611
## 214 -0.32850407 -12.543721 5.293305 -1.459630 2.0219013
## 215 -0.75502258 -14.696346 5.288267 -1.653590 2.1030230
## 216 -1.07880966 -13.004247 5.105945 -1.647864 4.0237466
## 218 -0.82098055 -13.103567 5.327876 -1.543930 2.1820549
## 219 -1.02165125 -13.760451 5.327876 -1.671366 2.1427912
## 220 -0.96758403 -14.406260 5.030438 -1.671366 1.0483341
## 223 -0.59783700 -12.212827 5.303305 -1.605032 1.6731213
## 224 -0.89159812 -12.721137 5.147494 -1.710172 1.8527528
## 225 -0.26136476 -13.528896 5.472271 -1.653590 1.5303762
## 226 0.09531018 -13.205557 5.620401 -1.647864 1.9805094
## 227 -0.63487827 -12.813142 5.293305 -1.780911 2.3343863
## 228 -0.94160854 -14.696346 5.023881 -1.653590 1.9805094
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## 233 -0.96758403 -13.205557 5.187386 -1.751002 2.9135187
## 234 -0.79850770 -11.983227 5.407172 -1.448638 1.0483341
## 236 -0.84397007 -13.004247 5.081404 -1.724319 2.0219013
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## 241 -0.65392647 -11.983227 5.129899 -1.780911 1.6263611
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## 244 -0.30110509 -11.698625 5.365976 -1.671366 2.3713615
## 245 -1.38629436 -15.904641 4.663439 -1.571048 1.9805094
## 246 -0.51082562 -11.698625 5.583496 -1.710172 3.1563503
## 247 -1.07880966 -15.709974 5.093750 -1.751002 2.1820549
## 249 -0.03045921 -14.849365 5.609472 -1.724319 1.0483341
## 250 -0.51082562 -13.310348 5.303305 -1.518336 1.8088944
## 251 -0.82098055 -12.721137 5.257495 -1.625834 1.8527528
## 253 -0.41551544 -10.750945 5.342334 -1.724319 1.5303762
## 254 0.26236426 -12.721137 5.598422 -1.710172 1.8527528
## 255 -0.22314355 -11.191436 5.472271 -1.671366 1.0483341
## 256 -1.04982212 -15.523564 5.176150 -1.724319 0.4005958
## 257 -1.13943428 -15.523564 5.093750 -1.625834 1.0483341
## 258 -0.34249031 -10.802885 5.351858 -1.590122 2.5152196
## 260 -0.69314718 -13.310348 5.273000 -1.830294 2.3713615
## 261 -0.91629073 -13.004247 4.682131 -1.647864 1.8527528
## 262 -1.10866262 -14.135373 4.976734 -1.571048 2.5502306
## 263 -0.47803580 -13.004247 5.407172 -1.830294 1.9805094
## 264 -0.38566248 -8.191715 5.327876 -1.724319 2.5848812
## 265 -0.75502258 -14.406260 5.252273 -1.830294 1.2195081
## 267 -0.02020271 -13.310348 5.624018 -1.751002 2.0219013
## 268 -0.34249031 -13.103567 5.283204 -1.571048 1.9805094
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## 273 -0.22314355 -13.881545 5.594711 -1.647864 2.3713615
## 274 -0.57981850 -13.418078 5.159055 -1.647864 1.9805094
## 275 -0.26136476 -13.205557 5.342334 -1.780911 1.8527528
## 277 -0.57981850 -11.838035 5.075174 -1.696685 2.0219013
## 278 -0.41551544 -12.292758 5.579730 -1.710172 2.1030230
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## 282 -0.82098055 -12.458129 5.288267 -1.518336 1.1637797
## 283 -0.10536052 -12.907477 5.777652 -1.571048 3.3286939
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## 290 -0.71334989 -14.268559 5.327876 -1.590122 2.2591348
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## 294 -1.02165125 -15.709974 5.442418 -1.557281 1.8527528
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## 298 -0.96758403 -13.205557 5.521461 -1.751002 0.4005958
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## 301 -0.99425227 -12.721137 5.257495 -1.671366 1.8527528
## 302 -1.66073121 -11.311317 4.962845 -1.653590 2.1030230
## 303 -0.82098055 -13.310348 5.384495 -1.830294 1.7643559
## 304 -0.19845094 -11.499497 5.587249 -1.780911 1.5303762
## 305 -0.40047757 -15.904641 5.693732 -1.647864 1.7643559
## 306 -0.15082289 -11.133063 5.480639 -1.653590 1.8527528
## 307 -0.16251893 -11.191436 5.541264 -1.710172 2.3343863
## 308 -0.56211892 -12.458129 5.181784 -1.653590 1.8527528
## 311 -0.67334455 -11.019298 5.225747 -1.724319 1.8527528
## 312 -0.46203546 -8.417032 5.214936 -1.724319 1.8527528
## 313 -0.82098055 -14.406260 5.638355 -1.571048 1.5303762
## 314 -0.77652879 -15.523564 5.236442 -1.518336 1.5303762
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## 316 -0.52763274 -13.310348 5.332719 -1.590122 0.4005958
## 317 -0.37106368 -13.418078 5.505332 -1.751002 1.8088944
## 320 -0.91629073 -11.630963 5.323010 -1.459630 1.1637797
## 321 -1.02165125 -12.292758 4.976734 -1.590122 0.4005958
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## 323 -0.46203546 -13.418078 5.424950 -1.830294 1.9805094
## 324 -0.71334989 -14.849365 5.135798 -1.780911 1.8527528
## 325 -0.47803580 -12.907477 5.472271 -1.571048 1.8959582
## 326 -0.40047757 -14.006447 5.389072 -1.518336 2.1030230
## 327 -0.27443685 -11.838035 5.609472 -1.871938 2.3343863
## 329 -0.61618614 -16.321511 5.278115 -1.868851 2.1427912
## 330 -0.79850770 -11.838035 5.209486 -1.780911 2.3343863
## 331 -1.17118298 -14.406260 5.087596 -1.605032 1.8959582
## 332 -1.02165125 -12.543721 5.411646 -1.571048 1.7643559
## 333 -0.21072103 -12.907477 5.552960 -1.647864 2.6867663
## Resistin Cortisol E4
## 1 -16.475315 10.0 1
## 2 -16.025283 12.0 2
## 3 -16.475315 10.0 2
## 5 -11.092838 11.0 1
## 6 -11.291369 13.0 2
## 7 -20.660678 4.9 1
## 8 -6.048172 13.0 1
## 9 -28.434991 12.0 1
## 11 -11.291369 6.8 2
## 12 -14.824999 12.0 1
## 14 -16.954608 15.0 2
## 16 -15.202379 12.0 2
## 17 -10.901667 12.0 1
## 18 -24.395099 0.1 2
## 19 -16.475315 10.0 1
## 20 -10.717434 18.0 2
## 21 -14.824999 26.0 2
## 22 -32.139553 14.0 1
## 23 -16.954608 16.0 2
## 24 -22.351393 7.8 1
## 25 -23.322142 8.6 2
## 26 -13.807280 14.0 2
## 28 -19.235033 8.9 1
## 29 -24.395099 15.0 2
## 30 -22.351393 1.8 1
## 31 -18.014017 19.0 1
## 34 -18.014017 14.0 2
## 35 -15.202379 14.0 2
## 36 -14.467762 9.8 1
## 37 -16.025283 14.0 2
## 38 -26.925298 9.5 1
## 39 -23.322142 15.0 1
## 40 -18.601960 12.0 1
## 41 -8.576675 13.0 1
## 42 -16.954608 11.0 2
## 43 -25.588488 10.0 1
## 44 -9.592564 9.5 2
## 45 -12.782746 15.0 1
## 46 -16.954608 15.0 1
## 47 -17.466301 15.0 1
## 48 -18.014017 15.0 1
## 50 -10.202587 9.8 1
## 51 -11.092838 10.0 1
## 53 -3.316155 11.0 1
## 55 -18.601960 15.0 2
## 56 -13.807280 12.0 1
## 57 -16.025283 11.0 1
## 59 -13.807280 11.0 2
## 60 -25.588488 7.0 1
## 61 -18.601960 17.0 1
## 62 -24.395099 7.1 1
## 63 -18.601960 13.0 1
## 64 -12.168957 10.0 2
## 65 -20.660678 13.0 1
## 67 -25.588488 18.0 2
## 68 -20.660678 15.0 1
## 69 -16.475315 7.4 1
## 70 -19.918999 14.0 1
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## 72 -11.712400 15.0 1
## 73 -9.737717 11.0 1
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## 75 -21.468210 16.0 1
## 76 -3.509845 12.0 2
## 77 -12.931637 17.0 2
## 78 -12.931637 11.0 2
## 80 -18.601960 11.0 1
## 81 -25.588488 12.0 1
## 82 -20.660678 14.0 2
## 83 -13.807280 12.0 1
## 84 -20.660678 14.0 2
## 85 -19.235033 12.0 1
## 86 -9.737717 12.0 1
## 88 -11.712400 12.0 2
## 90 -26.925298 9.9 1
## 93 -11.935945 12.0 2
## 94 -9.887603 18.0 1
## 95 -18.014017 5.9 1
## 96 -18.601960 11.0 2
## 97 -19.918999 8.2 2
## 98 -22.351393 8.3 1
## 99 -11.935945 15.0 1
## 100 -24.395099 6.5 2
## 103 -12.168957 16.0 1
## 104 -23.322142 14.0 2
## 105 -28.434991 9.0 1
## 107 -19.235033 10.0 2
## 108 -18.014017 13.0 1
## 109 -21.468210 14.0 1
## 110 -19.918999 11.0 1
## 111 -19.918999 9.1 2
## 112 -11.497723 13.0 1
## 113 -13.209714 13.0 2
## 114 -25.588488 13.0 2
## 115 -14.824999 29.0 2
## 117 -12.931637 13.0 1
## 118 -14.129014 11.0 1
## 121 -13.501240 18.0 2
## 123 -32.139553 9.5 2
## 124 -18.014017 13.0 1
## 126 -20.660678 10.0 2
## 128 -16.475315 8.9 2
## 129 -22.351393 8.4 2
## 130 -11.092838 4.0 1
## 131 -26.925298 10.0 1
## 132 -12.931637 12.0 2
## 133 -12.168957 13.0 2
## 134 -10.901667 12.0 2
## 135 -14.129014 8.7 1
## 136 -3.509845 29.0 2
## 137 -21.468210 9.7 2
## 139 -20.660678 13.0 2
## 140 -9.737717 22.0 2
## 141 -18.014017 12.0 2
## 143 -21.468210 7.1 1
## 144 -18.601960 12.0 2
## 145 -28.434991 9.8 1
## 146 -13.209714 15.0 2
## 147 -2.239355 11.0 2
## 148 -16.025283 9.7 1
## 149 -23.322142 8.9 1
## 152 -15.601770 14.0 2
## 153 -19.235033 8.8 1
## 154 -22.351393 8.9 1
## 155 -20.660678 8.1 2
## 156 -15.601770 9.0 1
## 157 -13.807280 11.0 1
## 158 -20.660678 7.0 2
## 159 -20.460441 9.3 1
## 160 -8.047964 8.5 1
## 161 -10.368242 13.0 1
## 162 -20.660678 9.1 1
## 163 -25.588488 11.0 2
## 165 -19.235033 14.0 2
## 166 -28.434991 13.0 1
## 167 -12.666051 10.0 1
## 168 -18.601960 7.7 1
## 169 -20.660678 12.0 1
## 170 -15.601770 14.0 1
## 171 -10.717434 13.0 1
## 172 -22.351393 8.5 1
## 174 -10.539746 12.0 1
## 175 -10.539746 18.0 1
## 176 -16.954608 17.0 1
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##
## $usekernel
## [1] TRUE
##
## $varnames
## [1] "MMP10" "GRO_alpha"
## [3] "TRAIL_R3" "Fibrinogen"
## [5] "PAI_1" "MMP7"
## [7] "NT_proBNP" "MIF"
## [9] "Pancreatic_polypeptide" "FAS"
## [11] "Eotaxin_3" "Gamma_Interferon_induced_Monokin"
## [13] "Thymus_Expressed_Chemokine_TECK" "TNF_RII"
## [15] "Alpha_1_Antitrypsin" "IGF_BP_2"
## [17] "Creatine_Kinase_MB" "MCP_2"
## [19] "Resistin" "Cortisol"
## [21] "E4"
##
## attr(,"class")
## [1] "NaiveBayes"NB_RFE_Tune$results## Variables ROC Sens Spec Accuracy Kappa ROCSD
## 1 1 0.7190930 0.2089286 0.9439474 0.7425417 0.1841923 0.08707182
## 2 21 0.7682472 0.6107143 0.7521053 0.7125865 0.3373127 0.10730612
## 3 41 0.7474013 0.6232143 0.7621053 0.7234432 0.3629843 0.11767270
## 4 61 0.7523731 0.6375000 0.7623684 0.7275641 0.3759538 0.10481520
## 5 81 0.7438064 0.6232143 0.7521053 0.7161681 0.3498289 0.10873176
## 6 101 0.7434164 0.6232143 0.7626316 0.7237179 0.3604692 0.10817177
## 7 121 0.7444220 0.5946429 0.7573684 0.7120472 0.3257190 0.11227352
## 8 127 0.7390414 0.5821429 0.7518421 0.7043549 0.3064041 0.11701325
## SensSD SpecSD AccuracySD KappaSD
## 1 0.1538549 0.04366712 0.05823576 0.1961714
## 2 0.2147482 0.13772656 0.10835094 0.2125839
## 3 0.2136979 0.14672583 0.11474056 0.2210172
## 4 0.2146244 0.14897335 0.12326383 0.2361289
## 5 0.2240579 0.14827831 0.11874534 0.2275762
## 6 0.2240579 0.13800005 0.11294404 0.2227129
## 7 0.2191980 0.11967690 0.09691544 0.2051666
## 8 0.2432769 0.12306395 0.10320115 0.2332805(NB_RFE_Train_ROCCurveAUC <- NB_RFE_Tune$results[NB_RFE_Tune$results$ROC==max(NB_RFE_Tune$results$ROC),
c("ROC")])## [1] 0.7682472##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
NB_RFE_Test <- data.frame(NB_RFE_Observed = PMA_PreModelling_Test$Class,
NB_RFE_Predicted = predict(NB_RFE_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
NB_RFE_Test_ROC <- roc(response = NB_RFE_Test$NB_RFE_Observed,
predictor = NB_RFE_Test$NB_RFE_Predicted.Impaired,
levels = rev(levels(NB_RFE_Test$NB_RFE_Observed)))
(NB_RFE_Test_ROCCurveAUC <- auc(NB_RFE_Test_ROC)[1])## [1] 0.75231481.5.10 Logistic Regression With RFE (LR_RFE)
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
Recursive Feature Elimination is a wrapper-style feature selection algorithm which searches for a subset of features by starting with all features in the training data set and successfully removing features until the desired number remains. The algorithm repeatedly fits a given machine learning algorithm used in the core of the model, ranks features by importance, discards the least important features, and re-fits the model. Features are scored either using importance scores relevant to the provided machine learning model or by applying statistical methods.
[A] The logistic regression model from the stats package was implemented with recursive feature elimination through the caret package.
[B] The model does not contain any hyperparameter.
[C] Recursive feature elimination was applied across a range of variable subset sizes ranging from 1 to 127:
[C.1] The variable subset with the best cross-validated performance was 21 with the top 5 variables identified as:
[C.1.1] Cortisol variable (numeric)
[C.1.2] RANTES variable (numeric)
[C.1.3] I_309 variable (numeric)
[C.1.4] MCP_2 variable (numeric)
[C.1.5] Clusterin_Apo_J variable (numeric)
[D] The cross-validated model performance of the final model is summarized as follows:
[D.1] Final model configuration variable subset=21
[D.2] ROC Curve AUC = 0.80296
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.86921
##################################
# Running the logistic regression model
# by setting the caret method to 'glm'
# with implementation of recursive feature elimination
##################################
KFold_RFEControl$functions <- lrFuncs
KFold_RFEControl$functions$summary <- FiveMetricsSummary
set.seed(12345678)
LR_RFE_Tune <- caret::rfe(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
sizes = VariableSubset,
metric = "ROC",
rfeControl = KFold_RFEControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
LR_RFE_Tune##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (10 fold)
##
## Resampling performance over subset size:
##
## Variables ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD
## 1 0.6415 0.02679 1.0000 0.7342 0.03654 0.06950 0.05663 0.00000
## 21 0.8030 0.56250 0.8755 0.7902 0.45093 0.07628 0.12235 0.04509
## 41 0.7884 0.56429 0.7939 0.7306 0.34840 0.08024 0.16392 0.11014
## 61 0.6941 0.52143 0.7787 0.7082 0.28982 0.07069 0.10708 0.06353
## 81 0.7082 0.55000 0.7682 0.7088 0.30000 0.07954 0.15878 0.07380
## 101 0.6967 0.52143 0.7576 0.6932 0.26488 0.08321 0.16577 0.09132
## 121 0.7055 0.55000 0.7626 0.7045 0.29461 0.06840 0.14380 0.08269
## 127 0.7073 0.57857 0.7626 0.7122 0.31951 0.06575 0.14536 0.08269
## AccuracySD KappaSD Selected
## 0.01806 0.07728
## 0.03639 0.10371 *
## 0.08904 0.19484
## 0.04230 0.09026
## 0.06390 0.15137
## 0.07411 0.17107
## 0.05400 0.11994
## 0.06290 0.14129
##
## The top 5 variables (out of 21):
## Cortisol, RANTES, I_309, MCP_2, Clusterin_Apo_JLR_RFE_Tune$fit##
## Call: glm(formula = Class ~ ., family = "binomial", data = tmp)
##
## Coefficients:
## (Intercept) Cortisol
## 25.28358 -0.17458
## RANTES I_309
## 1.81740 0.69368
## MCP_2 Clusterin_Apo_J
## -0.86484 -6.62826
## Fetuin_A Apolipoprotein_A1
## -3.50171 2.88160
## VEGF Protein_S
## 1.20472 1.68572
## Cystatin_C PAI_1
## 5.43369 -2.90334
## SOD Leptin
## -3.22998 1.20938
## Complement_Factor_H C_Reactive_Protein
## 0.09791 -0.21416
## Fatty_Acid_Binding_Protein TGF_alpha
## -1.31378 -0.34938
## Thrombopoietin Myoglobin
## -0.51341 0.21488
## Betacellulin MMP_2
## -0.06691 -0.50738
##
## Degrees of Freedom: 266 Total (i.e. Null); 245 Residual
## Null Deviance: 313.3
## Residual Deviance: 153.4 AIC: 197.4LR_RFE_Tune$results## Variables ROC Sens Spec Accuracy Kappa ROCSD
## 1 1 0.6415367 0.02678571 1.0000000 0.7341779 0.03653678 0.06949685
## 2 21 0.8029605 0.56250000 0.8755263 0.7902015 0.45093180 0.07627619
## 3 41 0.7884398 0.56428571 0.7939474 0.7305963 0.34840368 0.08024416
## 4 61 0.6940742 0.52142857 0.7786842 0.7082214 0.28982065 0.07068925
## 5 81 0.7082213 0.55000000 0.7681579 0.7087709 0.30000469 0.07954371
## 6 101 0.6966729 0.52142857 0.7576316 0.6932438 0.26488351 0.08321054
## 7 121 0.7055381 0.55000000 0.7626316 0.7045075 0.29461432 0.06840188
## 8 127 0.7073308 0.57857143 0.7626316 0.7121998 0.31950527 0.06575141
## SensSD SpecSD AccuracySD KappaSD
## 1 0.05662589 0.00000000 0.01806173 0.07727694
## 2 0.12235003 0.04508891 0.03638724 0.10370583
## 3 0.16392300 0.11014310 0.08903578 0.19483605
## 4 0.10707670 0.06352779 0.04229518 0.09025537
## 5 0.15878471 0.07380368 0.06390018 0.15136651
## 6 0.16577141 0.09131617 0.07410602 0.17106952
## 7 0.14379651 0.08268725 0.05400302 0.11993918
## 8 0.14536489 0.08268725 0.06290438 0.14129499(LR_RFE_Train_ROCCurveAUC <- LR_RFE_Tune$results[LR_RFE_Tune$results$ROC==max(LR_RFE_Tune$results$ROC),
c("ROC")])## [1] 0.8029605##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
LR_RFE_Test <- data.frame(LR_RFE_Observed = PMA_PreModelling_Test$Class,
LR_RFE_Predicted = predict(LR_RFE_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
LR_RFE_Test_ROC <- roc(response = LR_RFE_Test$LR_RFE_Observed,
predictor = LR_RFE_Test$LR_RFE_Predicted.Impaired,
levels = rev(levels(LR_RFE_Test$LR_RFE_Observed)))
(LR_RFE_Test_ROCCurveAUC <- auc(LR_RFE_Test_ROC)[1])## [1] 0.8692131.5.11 Support Vector Machine - Radial Basis Function Kernel With RFE (SVM_R_RFE)
Support Vector Machine plots each observation in an N-dimensional space corresponding to the number of features in the data set and finds a hyperplane that maximally separates the different classes by a maximally large margin (which is defined as the distance between the hyperplane and the closest data points from each class). The algorithm applies kernel transformation by mapping non-linearly separable data using the similarities between the points in a high-dimensional feature space for improved discrimination.
Recursive Feature Elimination is a wrapper-style feature selection algorithm which searches for a subset of features by starting with all features in the training data set and successfully removing features until the desired number remains. The algorithm repeatedly fits a given machine learning algorithm used in the core of the model, ranks features by importance, discards the least important features, and re-fits the model. Features are scored either using importance scores relevant to the provided machine learning model or by applying statistical methods.
[A] The support vector machine (radial basis function kernel) model from the kernlab package was implemented with recursive feature elimination through the caret package.
[B] The model contains 2 hyperparameters:
[B.1] sigma = sigma held constant at a value of 0.00455
[B.2] C = cost made to vary across a range of 10 default values
[C] Recursive feature elimination was applied across a range of variable subset sizes ranging from 1 to 127:
[C.1] The variable subset with the best cross-validated performance was 121 with the top 5 variables identified as:
[C.1.1] MMP10 variable (numeric)
[C.1.2] GRO_alpha variable (numeric)
[C.1.3] TRAIL_R3 variable (numeric)
[C.1.4] Fibrinogen variable (numeric)
[C.1.5] PAI_1 variable (numeric)
[D] The cross-validated model performance of the final model is summarized as follows:
[D.1] Final model configuration involves sigma=0.00455, C=8 and variable subset=121
[D.2] ROC Curve AUC = 0.84155
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.85995
##################################
# Running the support vector machine (radial basis function kernel) model
# by setting the caret method to 'svmRadial'
# with implementation of recursive feature elimination
##################################
KFold_RFEControl$functions <- caretFuncs
KFold_RFEControl$functions$summary <- FiveMetricsSummary
KFold_RFETrainControl <- trainControl(method = "cv",
verboseIter = FALSE,
classProbs = TRUE,
allowParallel = FALSE)
set.seed(12345678)
SVM_R_RFE_Tune <- caret::rfe(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
method = "svmRadial",
metric = "ROC",
tuneLength = 10,
preProc = c("center", "scale"),
trControl = KFold_RFETrainControl,
sizes = VariableSubset,
rfeControl = KFold_RFEControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
SVM_R_RFE_Tune##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (10 fold)
##
## Resampling performance over subset size:
##
## Variables ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD
## 1 0.6601 0.02857 0.9900 0.7272 0.02546 0.13251 0.06023 0.02108
## 21 0.7625 0.48750 0.9432 0.8182 0.47705 0.14977 0.22447 0.06168
## 41 0.8337 0.49643 0.9068 0.7946 0.42860 0.10199 0.21541 0.06400
## 61 0.8399 0.53571 0.9326 0.8245 0.50350 0.09070 0.20893 0.04308
## 81 0.8402 0.52321 0.9066 0.8021 0.45772 0.09906 0.19270 0.07614
## 101 0.8332 0.49643 0.9329 0.8138 0.46519 0.08115 0.24554 0.05401
## 121 0.8416 0.53929 0.9074 0.8067 0.46317 0.08825 0.26011 0.05787
## 127 0.8400 0.50714 0.9174 0.8060 0.45494 0.09705 0.22559 0.06928
## AccuracySD KappaSD Selected
## 0.02866 0.09407
## 0.08411 0.25302
## 0.06225 0.19226
## 0.05577 0.17978
## 0.05138 0.13790
## 0.08346 0.26062
## 0.07806 0.24220 *
## 0.07662 0.22004
##
## The top 5 variables (out of 121):
## MMP10, GRO_alpha, TRAIL_R3, Fibrinogen, PAI_1SVM_R_RFE_Tune$fit## Support Vector Machines with Radial Basis Function Kernel
##
## 267 samples
## 121 predictors
## 2 classes: 'Impaired', 'Control'
##
## Pre-processing: centered (121), scaled (121)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 240, 241, 241, 240, 241, 240, ...
## Resampling results across tuning parameters:
##
## C Accuracy Kappa
## 0.25 0.8058913 0.4883727
## 0.50 0.8020452 0.4799836
## 1.00 0.7986162 0.4619809
## 2.00 0.8131665 0.4834344
## 4.00 0.8019129 0.4249401
## 8.00 0.7945055 0.4078516
## 16.00 0.7908018 0.3938628
## 32.00 0.7869556 0.3877159
## 64.00 0.7945055 0.4157865
## 128.00 0.7870981 0.3701294
##
## Tuning parameter 'sigma' was held constant at a value of 0.004773811
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.004773811 and C = 2.SVM_R_RFE_Tune$results## Variables ROC Sens Spec Accuracy Kappa ROCSD
## 1 1 0.6600893 0.02857143 0.9900000 0.7271775 0.0254649 0.13250867
## 2 21 0.7624765 0.48750000 0.9431579 0.8181929 0.4770529 0.14976671
## 3 41 0.8337500 0.49642857 0.9068421 0.7945767 0.4286033 0.10198864
## 4 61 0.8398966 0.53571429 0.9326316 0.8245116 0.5034982 0.09069509
## 5 81 0.8401504 0.52321429 0.9065789 0.8021266 0.4577169 0.09906229
## 6 101 0.8332425 0.49642857 0.9328947 0.8137973 0.4651928 0.08114792
## 7 121 0.8415508 0.53928571 0.9073684 0.8066545 0.4631669 0.08825037
## 8 127 0.8400235 0.50714286 0.9173684 0.8059626 0.4549365 0.09705472
## SensSD SpecSD AccuracySD KappaSD
## 1 0.06023386 0.02108185 0.02865582 0.09407459
## 2 0.22446870 0.06168108 0.08410730 0.25301541
## 3 0.21540712 0.06399542 0.06224683 0.19226314
## 4 0.20892772 0.04308438 0.05576925 0.17978225
## 5 0.19270460 0.07614166 0.05137542 0.13790138
## 6 0.24553842 0.05400617 0.08346028 0.26062474
## 7 0.26011292 0.05786549 0.07805508 0.24219532
## 8 0.22559445 0.06928381 0.07662446 0.22003693(SVM_R_RFE_Train_ROCCurveAUC <- SVM_R_RFE_Tune$results[SVM_R_RFE_Tune$results$ROC==max(SVM_R_RFE_Tune$results$ROC),
c("ROC")])## [1] 0.8415508##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
SVM_R_RFE_Test <- data.frame(SVM_R_RFE_Observed = PMA_PreModelling_Test$Class,
SVM_R_RFE_Predicted = predict(SVM_R_RFE_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
SVM_R_RFE_Test_ROC <- roc(response = SVM_R_RFE_Test$SVM_R_RFE_Observed,
predictor = SVM_R_RFE_Test$SVM_R_RFE_Predicted.Impaired,
levels = rev(levels(SVM_R_RFE_Test$SVM_R_RFE_Observed)))
(SVM_R_RFE_Test_ROCCurveAUC <- auc(SVM_R_RFE_Test_ROC)[1])## [1] 0.85995371.5.12 K-Nearest Neighbors With RFE (KNN_RFE)
K-Nearest Neighbors works on the similarity principle which assumes that every data point falling in near to each other belong to the same class. The algorithm therefore assigns an unclassified sample point the classification of the nearest of a set of previously classified points.
Recursive Feature Elimination is a wrapper-style feature selection algorithm which searches for a subset of features by starting with all features in the training data set and successfully removing features until the desired number remains. The algorithm repeatedly fits a given machine learning algorithm used in the core of the model, ranks features by importance, discards the least important features, and re-fits the model. Features are scored either using importance scores relevant to the provided machine learning model or by applying statistical methods.
[A] The k-nearest neighbors model was implemented with recursive feature elimination through the caret package.
[B] The model contains 1 hyperparameter:
[B.1] k = number of neighbors made to vary across a range of 10 default values
[C] Recursive feature elimination was applied across a range of variable subset sizes ranging from 1 to 127:
[C.1] The variable subset with the best cross-validated performance was 41 with the top 5 variables identified as:
[C.1.1] MMP10 variable (numeric)
[C.1.2] GRO_alpha variable (numeric)
[C.1.3] TRAIL_R3 variable (numeric)
[C.1.4] Fibrinogen variable (numeric)
[C.1.5] PAI_1 variable (numeric)
[D] The cross-validated model performance of the final model is summarized as follows:
[D.1] Final model configuration involves k=23 and variable subset=41
[D.2] ROC Curve AUC = 0.79215
[E] The independent test model performance of the final model is summarized as follows:
[E.1] ROC Curve AUC = 0.84143
##################################
# Running the k-nearest neighbors model
# by setting the caret method to 'knn'
# with implementation of recursive feature elimination
##################################
KFold_RFEControl$functions <- caretFuncs
KFold_RFEControl$functions$summary <- FiveMetricsSummary
KFold_RFETrainControl <- trainControl(method = "cv",
verboseIter = FALSE,
classProbs = TRUE,
allowParallel = FALSE)
set.seed(12345678)
KNN_RFE_Tune <- caret::rfe(x = PMA_PreModelling_Train[,!names(PMA_PreModelling_Train) %in% c("Class")],
y = PMA_PreModelling_Train$Class,
method = "knn",
metric = "ROC",
tuneLength = 10,
preProc = c("center", "scale"),
trControl = KFold_RFETrainControl,
sizes = VariableSubset,
rfeControl = KFold_RFEControl)
##################################
# Reporting the cross-validation results
# for the train set
##################################
KNN_RFE_Tune##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (10 fold)
##
## Resampling performance over subset size:
##
## Variables ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD
## 1 0.7133 0.3964 0.8813 0.7491 0.3014 0.08153 0.1472 0.05522
## 21 0.7568 0.4161 0.9379 0.7951 0.3940 0.12900 0.2531 0.07130
## 41 0.7922 0.4143 0.9534 0.8062 0.4120 0.11749 0.2559 0.05628
## 61 0.7780 0.3054 0.9229 0.7544 0.2699 0.10890 0.1860 0.09598
## 81 0.7851 0.3607 0.9432 0.7839 0.3574 0.11967 0.1577 0.06968
## 101 0.7794 0.3321 0.9539 0.7837 0.3319 0.11815 0.1978 0.03788
## 121 0.7611 0.2339 0.9271 0.7377 0.1909 0.12715 0.1853 0.07551
## 127 0.7711 0.2750 0.9234 0.7465 0.2372 0.13122 0.1358 0.08669
## AccuracySD KappaSD Selected
## 0.05439 0.1650
## 0.08946 0.2676
## 0.07162 0.2409 *
## 0.09683 0.2413
## 0.07482 0.2026
## 0.06240 0.2232
## 0.07824 0.2275
## 0.06278 0.1420
##
## The top 5 variables (out of 41):
## MMP10, GRO_alpha, TRAIL_R3, Fibrinogen, PAI_1KNN_RFE_Tune$fit## k-Nearest Neighbors
##
## 267 samples
## 41 predictor
## 2 classes: 'Impaired', 'Control'
##
## Pre-processing: centered (41), scaled (41)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 240, 241, 241, 239, 241, 241, ...
## Resampling results across tuning parameters:
##
## k Accuracy Kappa
## 5 0.7980057 0.4217501
## 7 0.7947192 0.4291988
## 9 0.7948616 0.4266179
## 11 0.8028388 0.4401765
## 13 0.8138278 0.4665307
## 15 0.8135429 0.4537696
## 17 0.8397538 0.5157486
## 19 0.8247965 0.4751949
## 21 0.8360501 0.5056444
## 23 0.8322039 0.4919978
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was k = 17.KNN_RFE_Tune$results## Variables ROC Sens Spec Accuracy Kappa ROCSD
## 1 1 0.7133130 0.3964286 0.8813158 0.7491249 0.3013735 0.08153297
## 2 21 0.7567763 0.4160714 0.9378947 0.7951262 0.3939577 0.12900410
## 3 41 0.7921523 0.4142857 0.9534211 0.8062475 0.4120084 0.11749349
## 4 61 0.7779911 0.3053571 0.9228947 0.7543549 0.2699427 0.10889505
## 5 81 0.7851316 0.3607143 0.9431579 0.7838726 0.3574155 0.11966688
## 6 101 0.7794267 0.3321429 0.9539474 0.7837302 0.3318665 0.11814945
## 7 121 0.7610526 0.2339286 0.9271053 0.7377289 0.1909460 0.12715001
## 8 127 0.7710879 0.2750000 0.9234211 0.7465201 0.2372464 0.13122350
## SensSD SpecSD AccuracySD KappaSD
## 1 0.1472056 0.05522067 0.05438564 0.1650017
## 2 0.2531057 0.07129804 0.08945859 0.2676203
## 3 0.2558555 0.05627793 0.07161739 0.2408532
## 4 0.1860440 0.09597991 0.09683044 0.2413189
## 5 0.1576651 0.06968248 0.07482452 0.2026297
## 6 0.1978126 0.03787707 0.06240487 0.2231843
## 7 0.1852807 0.07551047 0.07824282 0.2275205
## 8 0.1357874 0.08669090 0.06277844 0.1420277(KNN_RFE_Train_ROCCurveAUC <- KNN_RFE_Tune$results[KNN_RFE_Tune$results$ROC==max(KNN_RFE_Tune$results$ROC),
c("ROC")])## [1] 0.7921523##################################
# Independently evaluating the model and
# reporting the independent evaluation results
# on the test set
##################################
KNN_RFE_Test <- data.frame(KNN_RFE_Observed = PMA_PreModelling_Test$Class,
KNN_RFE_Predicted = predict(KNN_RFE_Tune,
PMA_PreModelling_Test[,!names(PMA_PreModelling_Test) %in% c("Class")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
KNN_RFE_Test_ROC <- roc(response = KNN_RFE_Test$KNN_RFE_Observed,
predictor = KNN_RFE_Test$KNN_RFE_Predicted.Impaired,
levels = rev(levels(KNN_RFE_Test$KNN_RFE_Observed)))
(KNN_RFE_Test_ROCCurveAUC <- auc(KNN_RFE_Test_ROC)[1])## [1] 0.84143521.6 Model Evaluation Summary
Model performance comparison:
[A] Models which applied recursive feature elimination to select a subset of informative predictors performed better than those which utilized the full set of predictors.
[A.1] RF: Random Forest (randomForest package)
[A.1.1] RF_FULL (Random Forest Without Recursive Feature Elimination)
[A.1.1.1] Cross-Validation ROC Curve AUC = 0.78268
[A.1.1.2] Test ROC Curve AUC = 0.79803
[A.1.2] RF_RFE (Random Forest With Recursive Feature Elimination)
[A.1.2.1] Cross-Validation ROC Curve AUC = 0.81534
[A.1.2.2] Test ROC Curve AUC = 0.84201
[A.2] LDA: Linear Discriminant Analysis (MASS package)
[A.2.1] LDA_FULL (Linear Discriminant Analysis Without Recursive Feature Elimination)
[A.2.1.1] Cross-Validation ROC Curve AUC = 0.80151
[A.2.1.2] Test ROC Curve AUC = 0.77199
[A.2.2] LDA_RFE (Linear Discriminant Analysis With Recursive Feature Elimination)
[A.2.2.1] Cross-Validation ROC Curve AUC = 0.83569
[A.2.2.2] Test ROC Curve AUC = 0.85301
[A.3] NB: Naive Bayes (klaR package)
[A.3.1] NB_FULL (Naive Bayes Without Recursive Feature Elimination)
[A.3.1.1] Cross-Validation ROC Curve AUC = 0.73904
[A.3.1.2] Test ROC Curve AUC = 0.68055
[A.3.2] NB_RFE (Naive Bayes With Recursive Feature Elimination)
[A.3.2.1] Cross-Validation ROC Curve AUC = 0.76825
[A.3.2.2] Test ROC Curve AUC = 0.75231
[A.4] LR: Logistic Regression (stats package)
[A.4.1] LR_FULL (Logistic Regression Without Recursive Feature Elimination)
[A.4.1.1] Cross-Validation ROC Curve AUC = 0.70733
[A.4.1.2] Test ROC Curve AUC = 0.77199
[A.4.2] LR_RFE (Logistic Regression With Recursive Feature Elimination)
[A.4.2.1] Cross-Validation ROC Curve AUC = 0.80296
[A.4.2.2] Test ROC Curve AUC = 0.86921
[A.5] SVM_R: Support Vector Machine - Radial Basis Function Kernel (kernlab package)
[A.5.1] SVM_R_FULL (Support Vector Machine - Radial Basis Function Kernel Without Recursive Feature Elimination)
[A.5.1.1] Cross-Validation ROC Curve AUC = 0.85690
[A.5.1.2] Test ROC Curve AUC = 0.82060
[A.5.2] SVM_R_RFE (Support Vector Machine - Radial Basis Function Kernel With Recursive Feature Elimination)
[A.5.2.1] Cross-Validation ROC Curve AUC = 0.84155
[A.5.2.2] Test ROC Curve AUC = 0.85995
[A.6] KNN: K-Nearest Neighbors (caret package)
[A.6.1] KNN_FULL (K-Nearest Neighbors Without Recursive Feature Elimination)
[A.6.1.1] Cross-Validation ROC Curve AUC = 0.79886
[A.6.1.2] Test ROC Curve AUC = 0.79167
[A.6.2] KNN_RFE (K-Nearest Neighbors With Recursive Feature Elimination)
[A.6.2.1] Cross-Validation ROC Curve AUC = 0.79215
[A.6.2.2] Test ROC Curve AUC = 0.84143
[B] The models applied with recursive feature elimination which demonstrated the best and most consistent ROC Curve AUC metrics are as follows:
[B.1] SVM_R_RFE: Support Vector Machine - Radial Basis Function Kernel (kernlab package)
[B.2] LDA_RFE: Linear Discriminant Analysis (MASS package)
[B.3] RF_RFE: Random Forest (randomForest package)
[C] The most informative predictors consistently identified based from recursive feature elimination were as follows:
[C.1] MMP10 variable (numeric)
[C.2] Cystatin_C variable (numeric)
[C.3] TRAIL_R3 variable (numeric)
[C.4] PAI_1 variable (numeric)
[C.5] GRO_alpha variable (numeric)
##################################
# Consolidating all evaluation results
# for the train and test sets
# using the ROC Curve AUC metric
##################################
Model <- c('RF_FULL','LDA_FULL','NB_FULL','LR_FULL','SVM_R_FULL','KNN_FULL','RF_RFE','LDA_RFE','NB_RFE','LR_RFE','SVM_R_RFE','KNN_RFE',
'RF_FULL','LDA_FULL','NB_FULL','LR_FULL','SVM_R_FULL','KNN_FULL','RF_RFE','LDA_RFE','NB_RFE','LR_RFE','SVM_R_RFE','KNN_RFE')
Set <- c(rep('Cross-Validation',12),rep('Test',12))
ROCCurveAUC <- c(RF_FULL_Train_ROCCurveAUC,
LDA_FULL_Train_ROCCurveAUC,
NB_FULL_Train_ROCCurveAUC,
LR_FULL_Train_ROCCurveAUC,
SVM_R_FULL_Train_ROCCurveAUC,
KNN_FULL_Train_ROCCurveAUC,
RF_RFE_Train_ROCCurveAUC,
LDA_RFE_Train_ROCCurveAUC,
NB_RFE_Train_ROCCurveAUC,
LR_RFE_Train_ROCCurveAUC,
SVM_R_RFE_Train_ROCCurveAUC,
KNN_RFE_Train_ROCCurveAUC,
RF_FULL_Test_ROCCurveAUC,
LDA_FULL_Test_ROCCurveAUC,
NB_FULL_Test_ROCCurveAUC,
LR_FULL_Test_ROCCurveAUC,
SVM_R_FULL_Test_ROCCurveAUC,
KNN_FULL_Test_ROCCurveAUC,
RF_RFE_Test_ROCCurveAUC,
LDA_RFE_Test_ROCCurveAUC,
NB_RFE_Test_ROCCurveAUC,
LR_RFE_Test_ROCCurveAUC,
SVM_R_RFE_Test_ROCCurveAUC,
KNN_RFE_Test_ROCCurveAUC)
ROCCurveAUC_Summary <- as.data.frame(cbind(Model,Set,ROCCurveAUC))
ROCCurveAUC_Summary$ROCCurveAUC <- as.numeric(as.character(ROCCurveAUC_Summary$ROCCurveAUC))
ROCCurveAUC_Summary$Set <- factor(ROCCurveAUC_Summary$Set,
levels = c("Cross-Validation",
"Test"))
ROCCurveAUC_Summary$Model <- factor(ROCCurveAUC_Summary$Model,
levels = c('RF_FULL',
'RF_RFE',
'LDA_FULL',
'LDA_RFE',
'NB_FULL',
'NB_RFE',
'LR_FULL',
'LR_RFE',
'SVM_R_FULL',
'SVM_R_RFE',
'KNN_FULL',
'KNN_RFE'))
print(ROCCurveAUC_Summary, row.names=FALSE)## Model Set ROCCurveAUC
## RF_FULL Cross-Validation 0.7826833
## LDA_FULL Cross-Validation 0.8015132
## NB_FULL Cross-Validation 0.7390414
## LR_FULL Cross-Validation 0.7073308
## SVM_R_FULL Cross-Validation 0.8569079
## KNN_FULL Cross-Validation 0.7988651
## RF_RFE Cross-Validation 0.8153477
## LDA_RFE Cross-Validation 0.8356861
## NB_RFE Cross-Validation 0.7682472
## LR_RFE Cross-Validation 0.8029605
## SVM_R_RFE Cross-Validation 0.8415508
## KNN_RFE Cross-Validation 0.7921523
## RF_FULL Test 0.7980324
## LDA_FULL Test 0.7719907
## NB_FULL Test 0.6805556
## LR_FULL Test 0.7719907
## SVM_R_FULL Test 0.8206019
## KNN_FULL Test 0.7916667
## RF_RFE Test 0.8420139
## LDA_RFE Test 0.8530093
## NB_RFE Test 0.7523148
## LR_RFE Test 0.8692130
## SVM_R_RFE Test 0.8599537
## KNN_RFE Test 0.8414352(ROCCurveAUC_Plot <- dotplot(Model ~ ROCCurveAUC,
data = ROCCurveAUC_Summary,
groups = Set,
main = "Classification Model Performance Comparison",
ylab = "Model",
xlab = "ROC Curve AUC",
auto.key = list(adj = 1),
type=c("p", "h"),
origin = 0,
alpha = 0.45,
pch = 16,
cex = 2))
2. References
[Book] Applied Predictive Modeling by Max Kuhn and Kjell Johnson
[Book] An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani
[Book] Multivariate Data Visualization with R by Deepayan Sarkar
[Book] Machine Learning by Samuel Jackson
[Book] Data Modeling Methods by Jacob Larget
[Book] Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson
[R Package] AppliedPredictiveModeling by Max Kuhn
[R Package] caret by Max Kuhn
[R Package] rpart by Terry Therneau and Beth Atkinson
[R Package] lattice by Deepayan Sarkar
[R Package] dplyr by Hadley Wickham
[R Package] moments by Lukasz Komsta and Frederick
[R Package] skimr by Elin Waring
[R Package] RANN by Sunil Arya, David Mount, Samuel Kemp and Gregory Jefferis
[R Package] corrplot by Taiyun Wei
[R Package] tidyverse by Hadley Wickham
[R Package] lares by Bernardo Lares
[R Package] DMwR by Luis Torgo
[R Package] gridExtra by Baptiste Auguie and Anton Antonov
[R Package] rattle by Graham Williams
[R Package] rpart.plot by Stephen Milborrow
[R Package] RColorBrewer by Erich Neuwirth
[R Package] stats by R Core Team
[R Package] pls by Kristian Hovde Liland
[R Package] nnet by Brian Ripley
[R Package] elasticnet by Hui Zou
[R Package] earth by Stephen Milborrow
[R Package] party by Torsten Hothorn
[R Package] kernlab by Alexandros Karatzoglou
[R Package] randomForest by Andy Liaw
[R Package] pROC by Xavier Robin
[R Package] mda by Trevor Hastie
[R Package] klaR by Christian Roever, Nils Raabe, Karsten Luebke, Uwe Ligges, Gero Szepannek, Marc Zentgraf and David Meyer
[R Package] pamr by Trevor Hastie, Rob Tibshirani, Balasubramanian Narasimhan and Gil Chu
[Article] The caret Package by Max Kuhn
[Article] A Short Introduction to the caret Package by Max Kuhn
[Article] Caret Package – A Practical Guide to Machine Learning in R by Selva Prabhakaran
[Article] Tuning Machine Learning Models Using the Caret R Package by Jason Brownlee
[Article] Lattice Graphs by Alboukadel Kassambara
[Article] A Tour of Machine Learning Algorithms by Jason Brownlee
[Article] Recursive Feature Elimination: What It Is and Why It Matters by Karin Kelley
[Article] Recursive Feature Elimination (RFE) Example in Python by Otabek Yorkinov
[Article] Recursive Feature Elimination (RFE) for Feature Selection in Python by Jason Brownlee
[Article] Getting started with Recursive Feature Elimination algorithm in Machine Learning by Daniel Mwanthi
[Article] Guide To Dimensionality Reduction With Recursive Feature Elimination by Vijaysinh Lendave
[Article] Decision Tree Algorithm Examples In Data Mining by Software Testing Help Team
[Article] 4 Types of Classification Tasks in Machine Learning by Jason Brownlee
[Article] Spot-Check Classification Machine Learning Algorithms in Python with scikit-learn by Jason Brownlee
[Article] An Introduction to Naive Bayes Algorithm for Beginners by Turing Team
[Article] How Naive Bayes Algorithm Works? by Selva Prabhakaran
[Article] Naive Bayes Classifiers by Geeks For Geeks Team
[Article] Machine Learning Tutorial: A Step-by-Step Guide for Beginners by Mayank Banoula
[Article] Discriminant Analysis Essentials in R by Alboukadel Kassambara
[Article] Linear Discriminant Analysis, Explained by Xiaozhou Yang
[Article] Classification Tree by BCCVL Team
[Article] K-Nearest Neighbors (KNN) Classification with scikit-learn by Datacamp Team
[Article] Mathematical explanation of K-Nearest Neighbour by Geeks for Geeks Team
[Article] What Is The K-Nearest Neighbors Algorithm? by IBM Team
[Article] Random Forest by BCCVL Team
[Article] Generalized Linear Model by BCCVL Team
[Article] Introduction to Support Vector Machines by Geeks for Geeks Team
[Article] Support Vector Machines: A Simple Explanation by Noel Bambrick
[Publication] Enhanced Recursive Feature Elimination by Xuewen Chen and Jong Cheol Jeong (International Conference on Machine Learning and Applications (ICMLA))
[Publication] Bagging Predictors by Leo Breiman (Machine Learning)
[Publication] The Use of Multiple Measurements in Taxonomic Problems by Ronald Fisher (Annals of Human Genetics)
[Publication] The Origins of Logistic Regression by JS Cramer (Econometrics eJournal)
[Publication] Who Discovered Bayes’s Theorem? by Stephen Stigler (The American Statistician)
[Publication] Nearest Neighbor Pattern Classification by T Cover and P Hart (Transactions of Information Theory)
[Publication] Support Vector Regression Machines by Harris Drucker, Chris Burges, Linda Kaufman, Alex Smola and Vladimir Vapnik (Advances in Neural Information Processing Systems)
[Course] Applied Data Mining and Statistical Learning by Penn State Eberly College of Science
[Course] Regression Methods by Penn State Eberly College of Science
[Course] Applied Regression Analysis by Penn State Eberly College of Science
[Course] Applied Data Mining and Statistical Learning by Penn State Eberly College of Science